The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Kwok Wing Chau - One of the best experts on this subject based on the ideXlab platform.
-
uncertainty analysis on hybrid double Feedforward Neural Network model for sediment load estimation with lube method
Water Resources Management, 2019Co-Authors: Xiaoyun Chen, Kwok Wing ChauAbstract:The assessment of uncertainty prediction has become a necessity for most modeling studies within the hydrology community. This paper addresses uncertainty analysis on a novel hybrid double Feedforward Neural Network (HDFNN) model for generating the sediment load prediction interval (PI). By using the Lower Upper Bound Estimation (LUBE) method, the lower and upper bounds are directly generated as outputs of Neural Network based models. Coverage Width-based Criterion (CWC) is employed as an objective function for searching high quality PIs. The LUBE-based model is then applied to estimate sediment loads of Muddy Creek in Montana of USA. Results demonstrate the suitability of HDFNN-LUBE model in producing PI in both 90% and 95% confidence levels (CL). It is capable of generating appropriate lower bounds of PIs with narrow intervals. Partitioning analysis reveals consistently excellent performances of HDFNN model in constructing PI in terms of low, medium and high loads. These results therefore verify the reliability and potentiality of the HDFNN model for sediment load estimation with uncertainty. LUBE shows its efficiency in uncertainty prediction as well, which could be used to quantify total uncertainty of data-driven models.
-
a hybrid double Feedforward Neural Network for suspended sediment load estimation
Water Resources Management, 2016Co-Authors: Xiaoyun Chen, Kwok Wing ChauAbstract:Estimation of suspended sediment loads (SSL) in rivers is an important issue in water resources management and planning. This study proposes a hybrid double Feedforward Neural Network (HDFNN) model for daily SSL estimation, by combining fuzzy pattern-recognition and continuity equation into a structure of double Neural Networks. A comparison is performed between HDFNN, multi-layer Feedforward Neural Network (MFNN), double parallel Feedforward Neural Network (DPFNN) and hybrid Feedforward Neural Network (HFNN) models. Based on a case study on the Muddy Creek in Montana of USA, it is found that the HDFNN model is strongly superior to the other three benchmarking models in terms of root mean squared error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSEC). HDFNN model demonstrates the best generalization and estimation ability due to its configuration and capability of physically dealing with different inputs. The peak value of SSL is closely estimated by the HDFNN model as well. The performances of HDFNN model in low and medium loads are satisfactory when investigated by partitioning analysis. Thus, the HDFNN is appropriate for modeling the sediment transport process with nonlinear, fuzzy and time-varying characteristics. It explores a practical alternative for use and can be recommended as an efficient estimation model for SSL.
Chilukuri K Mohan - One of the best experts on this subject based on the ideXlab platform.
-
training Feedforward Neural Networks using multi phase particle swarm optimization
International Conference on Neural Information Processing, 2002Co-Authors: Buthainah Sabeeh Noman Alkazemi, Chilukuri K MohanAbstract:The multi-phase particle swarm optimization algorithm (MPPSO) is a variant of the particle swarm optimization algorithm. It simultaneously evolves multiple groups of particles that change their search criterion when changing the phases, and also incorporates hill-climbing. This paper examines the applicability of MPPSO in training Feedforward Neural Network.
Qinghua Ling - One of the best experts on this subject based on the ideXlab platform.
-
a survey on metaheuristic optimization for random single hidden layer Feedforward Neural Network
Neurocomputing, 2019Co-Authors: Fei Han, Jing Jiang, Qinghua LingAbstract:Abstract Random single-hidden layer Feedforward Neural Network (RSLFN) is currently a popular learning algorithm proposed for improving traditional gradient-based model due to its fast learning speed and acceptable performance. For RSLFN, the input weights and/or other parameters are randomly initialized, and the other ones are iteratively or non-iteratively trained. However, the performance of RSLFN is sensitive to the number of hidden neurons and randomly initialized parameters. Numerous methods have been successfully employed to improve the RSLFN from various perspectives. Because of their favourable search ability, metaheuristic optimization approaches gradually attract more and more attentions. Metaheuristic algorithms usually formulate the random parameters of RSLFN into an optimization model, and then provide a near-optimum solution which could be converted into RSLFN with better generalization performance. The hybrid method for optimizing RSLFN therefore shows considerable potential in intelligent computing and artificial intelligence. However, there is no comprehensive survey on RSLFN with metaheuristic in the research area, which ultimately leads to lost opportunities for an advancement. This paper firstly introduces the basic principles of RSLFN along with several metaheuristic algorithms. Secondly, it provides a comprehensive survey of the state-of-the-art contributions in the area. Finally, current challenges are highlighted and promising research directions are also presented.
Junfei Qiao - One of the best experts on this subject based on the ideXlab platform.
-
hierarchical extreme learning machine for Feedforward Neural Network
Neurocomputing, 2014Co-Authors: Honggui Han, Lidan Wang, Junfei QiaoAbstract:An approach, named extended extreme learning machine (ELM), is proposed for training the weights of a class of hierarchical Feedforward Neural Network (HFNN). Unlike conventional single-hidden-layer Feedforward Networks (SLFNs), this hierarchical ELM (HELM) is based on the hierarchical structure which is capable of hierarchical learning of sequential information online, and one may simply choose hidden layers and then only need to adjust the output weights linking the hidden layer and the output layer. In such HELM implementations, the extended ELM provides better generalization performance during the learning process. Moreover, the proposed extended ELM method is efficient not only for HFNNs with sigmoid hidden nodes but also for HFNNs with radial basis function (RBF) hidden nodes. Finally, the HELM is applied to the activated sludge wastewater treatment processes (WWTPs) for predicting the water qualities. Experimental results and the performance comparison demonstrate the effectiveness of the proposed HELM.
-
a structure optimisation algorithm for Feedforward Neural Network construction
Neurocomputing, 2013Co-Authors: Junfei QiaoAbstract:This paper proposes a constructing-and-pruning (CP) approach to optimise the structure of a Feedforward Neural Network (FNN) with a single hidden layer. The number of hidden nodes or neurons is determined by their contribution ratios, which are calculated using a Fourier decomposition of the variance of the FNN's output. Hidden nodes with sufficiently small contribution ratios will be eliminated, while new nodes will be added when the FNN cannot satisfy certain design objectives. This procedure is similar to the growing and pruning processes observed in biological Neural Networks. The performance of the proposed method is evaluated using a number of examples: real-life date classification, dynamic system identification, and the key variables modelling in a wastewater treatment system. Experimental results show that the proposed method effectively optimises the Network structure and performs better than some existing algorithms.
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
-
A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control
IEEE Transactions on Neural Networks, 2011Co-Authors: Min Han, Jianchao Fan, Jun WangAbstract:A dynamic Feedforward Neural Network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.
-
a Feedforward Neural Network for multiple criteria decision making
Social Science Research Network, 1992Co-Authors: Jun Wang, Behnam MalakootiAbstract:Many complex real-world problems are characterized as decision making with multiple, conflicting and noncommensurate objectives. Because of the complexity of factors that are involved, it is usually difficult to derive a decision rule for determining the most desirable alternative. This paper is to demonstrate the potential role of artificial Neural Networks for multiple criteria decision making. This paper presents a Feedforward Neural Network for solving discrete multiple criteria decision problems under certainty. Starting with formulating multiple criteria decision problems under the theme of supervised learning, this paper specifies two types of multiattribute decision models, proposes a particular form of Feedforward Neural Network, analyzes some desirable properties associated with supervised learning, presents an improved learning algorithm and discusses results of illustrative examples and numerical simulation.
