GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

Tang, Anqiong; Quek, Chai; Ng, Geok See

doi:10.1016/j.eswa.2005.06.001

Cited by 100 publications

(31 citation statements)

References 27 publications

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“…The populations were initially randomized before the search process was resumed, and then searched to determine the encoded chromosomes to maximize the optimal fitness function, which was computed for each of the randomly originated chromosomes. Because designing the optimal fitness function plays a major role in improving the search space efficiently and effectively, an improper fitness function can easily be trapped in a local optimum and can decrease in search effectiveness [31]. It facilitates assigning the optimal fitness value for each chromosome.…”

Section: Gas-based Optimization Of Feature Selectionmentioning

confidence: 99%

“…This algorithm has been proven to be robust and effective in searching large spaces for a wide range of applications [31,32]. To minimize data redundancy, optimizing the features that are closely related to landslide occurrence was crucial because most of the segmented object features were not relevant to this study.…”

Section: Gas-based Optimization Of Feature Selectionmentioning

confidence: 99%

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Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

et al. 2015

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This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the OPEN ACCESS Remote Sens. 2015, 7 4319 simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

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Section: Gas-based Optimization Of Feature Selectionmentioning

confidence: 99%

Section: Gas-based Optimization Of Feature Selectionmentioning

confidence: 99%

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

et al. 2015

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“…Chen (1999) compared several popular training algorithms for tuning parameters of ANFIS membership functions. Tang et al (2005) proposed a hybrid system combining a fuzzy inference system and genetic algorithms to tune the parameters in the TSK fuzzy ANN. Shoorehdeli et al (2009) proposed a novel hybrid learning algorithm with stable learning laws for ANFIS as a system identifier and studied the stability of this algorithm.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Design and analysis of experiments in ANFIS modeling for stock price prediction

Alizadeh¹,

Gharakhani²,

Fotoohi³

et al. 2011

IJIEC

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At the computational point of view, a fuzzy system has a layered structure, similar to an artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling approach has preference to explain solutions over completely black-box models, such as ANN. In this paper, we implement the design of experiment (DOE) technique to identify the significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for stock price prediction.

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“…That is, FRNNs consists of an aggregate of a fuzzy rule composed of premise (if) and consequence (then) with a polynomial different from a polynomial type of other fuzzy rules such as (8) and Fig. 3.…”

Section: Fuzzy Relation Neural Networkmentioning

confidence: 99%

“…Genetic learning support different levels of complexity of learning starting from the simplest case of parameter optimization (parametric learning) to the situations of structural learning level of complexity of learning the rule set of a rule based system [7]. FNN combines the advantages of both fuzzy inference systems in processing granular information and uncertainty and neural networks coming with learning abilities by generating a knowledge base without the need for involving human knowledge [8]. Various methods have been proposed for identification of fuzzy "if-then" rules [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

Park¹,

Kim²,

Oh³

2013

Journal of Electrical Engineering and Technology

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-In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

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GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

Cited by 100 publications

References 27 publications

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

Design and analysis of experiments in ANFIS modeling for stock price prediction

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

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