The Experts below are selected from a list of 30222 Experts worldwide ranked by ideXlab platform
Chee Peng Lim - One of the best experts on this subject based on the ideXlab platform.
-
a Fuzzy Inference System based criterion referenced assessment model
Expert Systems With Applications, 2011Co-Authors: Kai Meng Tay, Chee Peng LimAbstract:The main aim of criterion-referenced assessment (CRA) is to report students' achievements in accordance with a set of references. In practice, a score is given to each test item (or task). The scores from different test items are added together and then projected or aggregated, usually linearly, to produce a total score. Each component score can be weighted before being added together in order to reflect the relative importance of each test item. In this paper, the use of a Fuzzy Inference System (FIS) as an alternative to the conventional addition or weighted addition in CRA is investigated. A novel FIS-based CRA model is presented, and two important properties, i.e., the monotonicity and sub-additivity properties, of the FIS-based CRA model are investigated. A case study relating to assessment of laboratory projects in a university is conducted. The results indicate the usefulness of the FIS-based CRA model in comparing and assessing students' performances with human linguistic terms. Implications of the importance of the monotonicity and sub-additivity properties of the FIS-based CRA model in undertaking general assessment problems are discussed.
-
enhancing Fuzzy Inference System based criterion referenced assessment with an application
European Conference on Modelling and Simulation, 2010Co-Authors: Kai Meng Tay, Chee Peng Lim, Tze Ling JeeAbstract:An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of Fuzzy sets, and Fuzzy rules. The aim of this paper is to demonstrate how an additional qualitative information, i.e., monotonicity property, can be exploited and extended to be part of an FIS designing procedure (i.e., Fuzzy sets and Fuzzy rules design). In this paper, the FIS is employed as an alternative to the use of addition in aggregating the scores from test items/tasks in a Criterion-Referenced Assessment (CRA) model. In order to preserve the monotonicity property, the sufficient conditions of the FIS is proposed. Our proposed FIS based CRA procedure can be viewed as an enhancement for the FIS based CRA procedure, where monotonicity property is preserved. We demonstrate the applicability of the proposed approach with a case study related to a laboratory project assessment task at a university, and the results indicate the usefulness of the proposed approach in the CRA domain.
Kai Meng Tay - One of the best experts on this subject based on the ideXlab platform.
-
a Fuzzy Inference System based criterion referenced assessment model
Expert Systems With Applications, 2011Co-Authors: Kai Meng Tay, Chee Peng LimAbstract:The main aim of criterion-referenced assessment (CRA) is to report students' achievements in accordance with a set of references. In practice, a score is given to each test item (or task). The scores from different test items are added together and then projected or aggregated, usually linearly, to produce a total score. Each component score can be weighted before being added together in order to reflect the relative importance of each test item. In this paper, the use of a Fuzzy Inference System (FIS) as an alternative to the conventional addition or weighted addition in CRA is investigated. A novel FIS-based CRA model is presented, and two important properties, i.e., the monotonicity and sub-additivity properties, of the FIS-based CRA model are investigated. A case study relating to assessment of laboratory projects in a university is conducted. The results indicate the usefulness of the FIS-based CRA model in comparing and assessing students' performances with human linguistic terms. Implications of the importance of the monotonicity and sub-additivity properties of the FIS-based CRA model in undertaking general assessment problems are discussed.
-
enhancing Fuzzy Inference System based criterion referenced assessment with an application
European Conference on Modelling and Simulation, 2010Co-Authors: Kai Meng Tay, Chee Peng Lim, Tze Ling JeeAbstract:An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of Fuzzy sets, and Fuzzy rules. The aim of this paper is to demonstrate how an additional qualitative information, i.e., monotonicity property, can be exploited and extended to be part of an FIS designing procedure (i.e., Fuzzy sets and Fuzzy rules design). In this paper, the FIS is employed as an alternative to the use of addition in aggregating the scores from test items/tasks in a Criterion-Referenced Assessment (CRA) model. In order to preserve the monotonicity property, the sufficient conditions of the FIS is proposed. Our proposed FIS based CRA procedure can be viewed as an enhancement for the FIS based CRA procedure, where monotonicity property is preserved. We demonstrate the applicability of the proposed approach with a case study related to a laboratory project assessment task at a university, and the results indicate the usefulness of the proposed approach in the CRA domain.
Engin Avci - One of the best experts on this subject based on the ideXlab platform.
-
an expert discrete wavelet adaptive network based Fuzzy Inference System for digital modulation recognition
Expert Systems With Applications, 2007Co-Authors: Engin Avci, Davut Hanbay, Asaf VarolAbstract:This paper presents a comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition. Here, in first stage, 20 different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, Symlets wavelet families. In second stage, the performance comparison of these feature extraction methods is performed by using a new Expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System (EDWANFIS). The digital modulated signals used in this experimental study are ASK8, FSK8, PSK8, QASK8. EDWANFIS structure consists of two parts. The first part is Discrete Wavelet Transform (DWT)-adaptive wavelet entropy and Adaptive Network Based Fuzzy Inference System for Automatic Digital Modulation Recognition (ADMR). The performance of this comparison System is evaluated by using total 800 digital modulated signals for each of these feature extraction methods. The performance comparison of these features extraction methods and the advantages and disadvantages of the methods are examined.
-
speech recognition using a wavelet packet adaptive network based Fuzzy Inference System
Expert Systems With Applications, 2006Co-Authors: Engin Avci, Zuhtu Hakan AkpolatAbstract:Abstract In this paper, an expert speech recognition System is presented. This paper especially deals with the combination of feature extraction and classification for real speech signals. A Wavelet packet adaptive network based Fuzzy Inference System (WPANFIS) model is developed in this study. WPANFIS consists of two layers: wavelet packet and adaptive network based Fuzzy Inference System. The wavelet packet layer is used for adaptive feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The performance of the developed System is evaluated by using noisy speech signals. Test results showing the effectiveness of the proposed speech recognition System are presented in the paper. The rate of correct classification is about 92% for the sample speech signals.
Hossein Bonakdari - One of the best experts on this subject based on the ideXlab platform.
-
performance evaluation of adaptive neural Fuzzy Inference System for sediment transport in sewers
Water Resources Management, 2014Co-Authors: Isa Ebtehaj, Hossein BonakdariAbstract:The application of models capable of estimating sediment transport in sewers has been a frequent practice in the past years. Considering the fact that predicting sediment transport within the sewer is a complex phenomenon, the existing equations used for predicting densimetric Froude number do not present similar results. Using Adaptive Neural Fuzzy Inference System (ANFIS) this article studies sediment transport in sewers. For this purpose, five different dimensionless groups including motion, transport, sediment, transport mode and flow resistance are introduced first and then the effects of various parameters in different groups on the estimation of the densimetric Froude number in the motion group are presented as six different models. To present the models, two states of grid partitioning and sub-clustering were used in Fuzzy Inference System (FIS) generation. Moreover, the training algorithms applied in this article include back propagation and hybrid. The results of the proposed models are compared with the experimental data and the existing equations. The results show that ANFIS models have greater accuracy than the existing sediment transport equations.
Goran Simunovic - One of the best experts on this subject based on the ideXlab platform.
-
an adaptive network based Fuzzy Inference System anfis for the forecasting the case of close price indices
Expert Systems With Applications, 2013Co-Authors: Ilija Svalina, Vjekoslav Galzina, Roberto Lujic, Goran SimunovicAbstract:The close price prediction model of the Zagreb Stock Exchange Crobex(R) index is presented in this paper. For the input/output data plan modeling the Crobex(R) index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5days in advance it predicts the Crobex(R) close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-Fuzzy Inference System method, representing the Fuzzy Inference System. It is of the essence to point out that for each day a separate Fuzzy Inference System is created by means of the adaptive neuro-Fuzzy Inference System method based on the same set of input/output data, the only difference being that for every separate Fuzzy Inference System different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex(R) index close price from 4 November 2010 to 24 January 2012 and the Crobex(R) index close price is predicted for the subsequent 5days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5days in advance and the Crobex(R) index close price is predicted in advance for the next 5days starting with the last day of the input/output data set. In that way the Crobex(R) index close prices are predicted until 19 October 2012 based on the Crobex(R) index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex(R) index close price showing that the approach is useful for predicting within its limits.
