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Chia-feng Juang - One of the best experts on this subject based on the ideXlab platform.
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Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans, 2007Co-Authors: Chia-feng Juang, Chia-ming ChangAbstract:A new classification approach for human body postures based on a neural Fuzzy Network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together. with the silhouette length-width ratio are used as features. The classifier is designed by a neural Fuzzy Network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed
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Recurrent Fuzzy Network design using hybrid evolutionary learning algorithms
Neurocomputing, 2007Co-Authors: Chia-feng Juang, I-fang ChungAbstract:This paper proposes a recurrent Fuzzy Network design using the hybridization of a multigroup genetic algorithm and particle swarm optimization (R-MGAPSO). The recurrent Fuzzy Network designed here is the Takagi-Sugeno-Kang (TSK)-type recurrent Fuzzy Network (TRFN), in which each Fuzzy rule comprises spatial and temporal sub-rules. Both the number of Fuzzy rules and the parameters in a TRFN are designed simultaneously by R-MGAPSO. In R-MGAPSO, the techniques of variable-length individuals and the local version of particle swarm optimization are incorporated into a genetic algorithm, where individuals with the same length constitute the same group, and there are multigroups in a population. Population evolution consists of three major operations: elite enhancement by particle swarm optimization, sub-rule alignment-based crossover, and mutation. To verify the performance of R-MGAPSO, dynamic plant and a continuous-stirred tank reactor controls are simulated. R-MGAPSO performance is also compared with genetic algorithms in these simulations. (c) 2006 Elsevier B.V. All rights reserved
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Temporal problems solved by dynamic Fuzzy Network based on genetic algorithm with variable-length chromosomes
Fuzzy Sets and Systems, 2004Co-Authors: Chia-feng JuangAbstract:Abstract In this paper, a dynamic Fuzzy Network and its design based on genetic algorithm with variable-length chromosomes is proposed. First, the dynamic Fuzzy Network constituted from a series of dynamic Fuzzy if–then rules is proposed. One characteristic of this Network is its ability to deal with temporal problems. Then, the proposed genetic algorithm with variable-length chromosomes is adopted into the design process as a means of allowing the application of the Network in situations where the actual desired output is unavailable. In the proposed genetic algorithm, the length of each chromosome varies with the number of rules coded in it. Using this algorithm, no pre-assignment of the number of rules in the dynamic Fuzzy Network is required, since it can always help to find the most suitable number of rules. All free parameters in the Network, including the spatial input partition, consequent parameters and feedback connection weights, are tuned concurrently. To further promote the design performance, genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation is proposed. In this algorithm, the elite individuals are directly reproduced to the next generation only when their averaged similarity value is smaller than a similarity threshold; otherwise, the elites are mutated to the next generation. To show the efficiency of this dynamic Fuzzy Network designed by genetic algorithm with variable-length chromosomes and relative-based mutated reproduction operation, two temporal problems are simulated. The simulated results and comparisons with recurrent neural and Fuzzy Networks verify the efficacy and efficiency of the proposed approach.
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a tsk type recurrent Fuzzy Network for dynamic systems processing by neural Network and genetic algorithms
IEEE Transactions on Fuzzy Systems, 2002Co-Authors: Chia-feng JuangAbstract:In this paper, a TSK-type recurrent Fuzzy Network (TRFN) structure is proposed. The proposal calls for the design of TRFN by either neural Network or genetic algorithms depending on the learning environment. A recurrent Fuzzy Network is described which develops from a series of recurrent Fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from Fuzzy firing strengths, back to both the Network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding Fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in Network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and a neural Network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, the TRFN-S displays both small Network size and high learning accuracy. For problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed Network structure of TRFN, TRFN-G, like TRFN-S, is characterized by high learning accuracy. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent Networks and design configurations, the efficiency of TRFN is verified.
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a recurrent self organizing neural Fuzzy inference Network
IEEE Transactions on Neural Networks, 1999Co-Authors: Chia-feng Juang, Chinteng LinAbstract:A recurrent self-organizing neural Fuzzy inference Network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist Network for realizing the basic elements and functions of dynamic Fuzzy inference, and may be considered to be constructed from a series of dynamic Fuzzy rules. The temporal relations embedded in the Network are built by adding some feedback connections representing the memory elements to a feedforward neural Fuzzy Network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a Fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural Fuzzy Network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of Fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent Networks are also made. Efficiency of the RSONFIN is verified from these results.
Chinteng Lin - One of the best experts on this subject based on the ideXlab platform.
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a hybrid of cooperative particle swarm optimization and cultural algorithm for neural Fuzzy Networks and its prediction applications
Systems Man and Cybernetics, 2009Co-Authors: Cheng-jian Lin, Cheng-hung Chen, Chinteng LinAbstract:This study presents an evolutionary neural Fuzzy Network, designed using the functional-link-based neural Fuzzy Network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural Networks as the consequent part of the Fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural Networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
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Satellite sensor image classification using cascaded architecture of neural Fuzzy Network
IEEE Transactions on Geoscience and Remote Sensing, 2000Co-Authors: Chinteng Lin, Yin-cheung LeeAbstract:Satellite sensor images usually contain many complex factors and mixed pixels, so a high classification accuracy is not easy to attain. Especially, for a nonhomogeneous region, gray values of satellite sensor images vary greatly and thus, direct statistic gray values fail to do the categorization task correctly. The goal of this paper is to develop a cascaded architecture of neural Fuzzy Networks with feature mapping (CNFM) to help the clustering of satellite sensor images. In the CNFM, a Kohonen's self-organizing feature map (SOFM) is used as a preprocessing layer for the reduction of feature domain, which combines original multi-spectral gray values, structural measurements from co-occurrence matrices, and spectrum features from wavelet decomposition. In addition to the benefit of dimensional reduction of feature space, Kohonen's SOFM can remove some noisy areas and prevent the following training process from being overoriented to the training patterns. The condensed measurements are then forwarded into a neural Fuzzy Network, which performs supervised learning for pattern classification. The proposed cascaded approach is an appropriate technique for handling the classification problem in areas that exhibit large spatial variation and interclass heterogeneity (e.g., urban-rural infringing areas). The CNFM is a general and useful structure that can give us favorable results in terms of classification accuracy and learning speed. Experimental results indicate that our structure can retain high accuracy of classification (90% in average), while the training time is substantially reduced if our system is compared to the commonly used backpropagation Network.
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a recurrent self organizing neural Fuzzy inference Network
IEEE Transactions on Neural Networks, 1999Co-Authors: Chia-feng Juang, Chinteng LinAbstract:A recurrent self-organizing neural Fuzzy inference Network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist Network for realizing the basic elements and functions of dynamic Fuzzy inference, and may be considered to be constructed from a series of dynamic Fuzzy rules. The temporal relations embedded in the Network are built by adding some feedback connections representing the memory elements to a feedforward neural Fuzzy Network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a Fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural Fuzzy Network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of Fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent Networks are also made. Efficiency of the RSONFIN is verified from these results.
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a recurrent self organizing neural Fuzzy inference Network
IEEE International Conference on Fuzzy Systems, 1997Co-Authors: Chia-feng Juang, Chinteng LinAbstract:A recurrent self-organizing neural Fuzzy inference Network (RSONFIN) is proposed in this paper. The RSONFIN is constructed from a series of dynamic Fuzzy rules. The temporal relations embedded in the Network are built by adding some feedback connections representing the memory elements to a feedforward neural Fuzzy Network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a Fuzzy rule. There are no hidden nodes (i.e., no membership functions and Fuzzy rules) initially in the RSONFIN. They are created online via concurrent structure identification (the construction of dynamic Fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural Fuzzy Network. Simulations on temporal problems are performed.
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FUZZ-IEEE - Application of neural Fuzzy Network to pulse compression with binary phase code
The 12th IEEE International Conference on Fuzzy Systems 2003. FUZZ '03., 1Co-Authors: Fun-bin Duh, Chia-feng Juang, Chinteng LinAbstract:To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural Network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural Network such as backpropagation (BP) Network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural Fuzzy Network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural Fuzzy Network (SONFIN) with supervised learning algorithm. Simulation results show that this neural Fuzzy Network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.
Jie Zhang - One of the best experts on this subject based on the ideXlab platform.
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Modelling and multi-objective optimal control of batch processes using recurrent neuro-Fuzzy Networks
International Journal of Automation and Computing, 2006Co-Authors: Jie ZhangAbstract:In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-Fuzzy Network, are presented. The recurrent neuro-Fuzzy Network, forms a “global” nonlinear long-range prediction model through the Fuzzy conjunction of a number of “local” linear dynamic models. Network output is fed back to Network input through one or more time delay units, which ensure that predictions from the recurrent neuro-Fuzzy Network are long-range. In building a recurrent neural Network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding Network weights. Process operational data is then used to train the Network. Membership functions of the local regimes are identified, and local models are discovered via Network training. Based on a recurrent neuro-Fuzzy Network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
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Modeling and optimal control of batch processes using recurrent neuro-Fuzzy Networks
IEEE Transactions on Fuzzy Systems, 2005Co-Authors: Jie ZhangAbstract:A recurrent neuro-Fuzzy Network based strategy for batch process modeling and optimal control is presented in this paper. The recurrent neuro-Fuzzy Network allows the construction of a "global" nonlinear long-range prediction model from the Fuzzy conjunction of a number of "local" linear dynamic models. In this recurrent neuro-Fuzzy Network, the Network output is fed back to the Network input through one or more time delay units. This particular structure ensures that predictions from a recurrent neuro-Fuzzy Network are long-range or multi-step-ahead predictions. Long-range predictions are particularly important for batch processes where the interest lies in the product quality and quantity at the end of a batch. To enhance batch process control and monitoring, a model capable of predicting accurately the product quality/quantity at the end of a batch is required. Process knowledge is used to initially partition the process nonlinear characteristics into several local operating regions and to aid in the initialization of the corresponding Network weights. Process input output data is then used to train the Network. Membership functions of the local regimes are identified and local models are discovered through Network training. An advantage of this recurrent neuro-Fuzzy Network model is that it is easy to interpret. This helps process operators in understanding the process characteristics. The proposed technique is applied to the modeling and optimal control of a fed-batch reactor.
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A Nonlinear Gain Scheduling Control Strategy Based on Neuro-Fuzzy Networks
Industrial & Engineering Chemistry Research, 2001Co-Authors: Jie ZhangAbstract:A nonlinear gain scheduling control strategy based on neuro-Fuzzy Network models is proposed. In neuro-Fuzzy-Network-based modeling, the process operation is partitioned into several Fuzzy operating regions, and within each region, a local linear model is used to model the process. The global model output is obtained through center-of-gravity defuzzification. Process knowledge is used to initially set up the Network structure, and process input−output data are used to train the Network. Based on a neuro-Fuzzy Network model, a nonlinear controller can be developed by combining several local linear controllers that are tuned on the basis of the local model parameters. This strategy represents a nonlinear gain scheduled controller. The techniques have been successfully applied to the modeling and control of pH dynamics in a simulated continuous stirred tank reactor.
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Recurrent neuro-Fuzzy Networks for nonlinear process modeling
IEEE transactions on neural networks, 1999Co-Authors: Jie Zhang, A.j. MorrisAbstract:A type of recurrent neuro-Fuzzy Network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several Fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several Fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the Network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of Fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-Fuzzy Network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.
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Recurrent neuro-Fuzzy Networks for the modelling and optimal control of batch processes
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 1Co-Authors: Jie ZhangAbstract:A recurrent neuro-Fuzzy Network based strategy for batch process modelling and optimal control is presented. The recurrent neuro-Fuzzy Network allows the construction of a "global" nonlinear long-range prediction model from the Fuzzy conjunction of a number of "local" linear dynamic models. In this recurrent neuro-Fuzzy Network, the Network output is fed back to the Network input through one or more time delay units. This particular structure ensures that predictions from a recurrent neuro-Fuzzy Network are long-range or multi-step-ahead predictions. Process knowledge is used to initially partition the process nonlinear characteristics into several local operating regions and to aid in the initialisation of the corresponding Network weights. Process input output data is then used to train the Network. Membership functions of the local regimes are identified and local models are discovered through Network training. In the paper, a recurrent neuro-Fuzzy Network is used to model a fed-batch reactor and to calculate the optimal feeding policy.
Cheng-jian Lin - One of the best experts on this subject based on the ideXlab platform.
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A functional neural Fuzzy Network for classification applications
Expert Systems with Applications, 2011Co-Authors: Cheng-jian Lin, Chi-yung LeeAbstract:Research highlights? A functional neural Fuzzy system model is proposed. We adopts a functional neural Network to the consequent part of the Fuzzy rules. ? Orthogonal polynomials and linearly independent functions are used for a functional expansion of the functional neural Network. ? An online learning algorithm, which consist of structure learning algorithm and parameter learning algorithm, is proposed. ? The average testing accuracy rates of the functional neural Fuzzy system in Iris data and wine classification data were 98.1% and 99.1%. This study presents a functional neural Fuzzy Network (FNFN) for classification applications. The proposed FNFN model adopts a functional neural Network (FLNN) to the consequent part of the Fuzzy rules. Orthogonal polynomials and linearly independent functions are used for a functional expansion of the FLNN. Thus, the consequent part of the proposed FNFN model is a nonlinear combination of input variables. The FNFN model can construct its structure and adapt its free parameters with online learning algorithms, which consist of structure learning algorithm and parameter learning algorithm. The structure learning algorithm is based on the entropy measure to determine the number of Fuzzy rules. The parameter learning algorithm, based on the gradient descent method, can adjust the shapes of the membership functions and the corresponding weights of the FLNN. Finally, the FNFN model is applied to various simulations. The simulation results for the Iris, Wisconsin breast cancer, and wine classifications show that FNFN model has superior performance than other models for classification applications.
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An efficient Symbiotic Taguchi-based Differential Evolution for neuro-Fuzzy Network design
Third International Workshop on Advanced Computational Intelligence, 2010Co-Authors: Cheng-jian Lin, Chia-hu Hsu, Chun-cheng PengAbstract:In this paper, we proposed a functional-link-based neural Fuzzy Network to improve the traditional TSK-type neural Fuzzy Network. Besides, an efficient evolutionary learning algorithm, called the Symbiotic Taguchi-based Modified Differential Evolution (STMDE), is proposed for the neural Fuzzy Networks design. Firstly, in order to avoid trapping in a local optimal solution and to ensure the searching capability of near global optimal solution, the STMDE adopts the Taguchi method to effectively search towards the best individual and employs an adaptive parameter control to adjust scaling factor which is called the Taguchi method. Moreover, the proposed STMDE introduces the concept of symbiotic evolution to improve the individual structure. Unlike the traditional individual that uses each one in a population as a full solution to a given problem, symbiotic evolution assumes that each individual in a population represents only a partial solution, while complex solutions combine several individuals in the population.
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a hybrid of cooperative particle swarm optimization and cultural algorithm for neural Fuzzy Networks and its prediction applications
Systems Man and Cybernetics, 2009Co-Authors: Cheng-jian Lin, Cheng-hung Chen, Chinteng LinAbstract:This study presents an evolutionary neural Fuzzy Network, designed using the functional-link-based neural Fuzzy Network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural Networks as the consequent part of the Fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural Networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
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An asymmetry subsethood‐based neural Fuzzy Network
Asian Journal of Control, 2008Co-Authors: Cheng-jian Lin, Tzu-chao Lin, Chin-ling LeeAbstract:This paper proposes a novel asymmetric subsethood-based neural Fuzzy Network (ASNFN) that identifies and controls nonlinear dynamic systems. ASNFN has the flexibility to handle both numeric and linguistic inputs. The numeric inputs in ASNFN are fuzzified by input nodes as tunable feature fuzzifiers. Connections in ASNFN are represented by pseudo-Gaussian Fuzzy sets which provide the neural Fuzzy Network with higher flexibility and attain more accurate optimization. An online self-constructing learning algorithm that is constructed and implemented in ASNFN consists of structural learning and parametric learning, and would create adaptive Fuzzy logic rules. Computer simulations illustrate the performance and capability of the proposed model in identifying a dynamic system, in Iris data classification, and in approximating a nonlinear function. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
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A Self-Constructing Neural Fuzzy Network with Dynamic-Form Symbiotic Evolution
Intelligent Automation & Soft Computing, 2007Co-Authors: Cheng-jian LinAbstract:Abstract In this paper, we propose aself-constructing neural Fuzzy Network with dynamic-form symbiotic evolution (SCNFN-DSE) for solving various problerns. A novel hybrid learning approach, which consists of the self-clustering algorithm (SCA) and the dynamic-form symbiotic evolution (DSE), is proposed for adjusting the parameters of neural Fuzzy Networks. First, the proposed SCA is used to identify a pazsimonious internal structure. The SCA is an online clustering method and is a distance-based connectionist clustering method. Second, the proposed DSE uses the sequential-search based dynamic evolution (SSDE) method. The better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that 1) the SCNFN-DSE model converges quickly; 2) the SCNFNDSE model requires a small number of population sizes; 3) the SCNFN-DSE model construct only 4 Fuzzy models in every generation.
Fun-bin Duh - One of the best experts on this subject based on the ideXlab platform.
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FUZZ-IEEE - Application of neural Fuzzy Network to pulse compression with binary phase code
The 12th IEEE International Conference on Fuzzy Systems 2003. FUZZ '03., 1Co-Authors: Fun-bin Duh, Chia-feng Juang, Chinteng LinAbstract:To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural Network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural Network such as backpropagation (BP) Network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural Fuzzy Network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural Fuzzy Network (SONFIN) with supervised learning algorithm. Simulation results show that this neural Fuzzy Network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.
