The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

Wang, Guijun; Suo, Chunfeng

doi:10.1007/s00521-016-2600-5

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…In 2018, Wang and Suo 22 gave an isolation layer optimization algorithm for the MIMO polygonal fuzzy neural network. However, the proposed algorithm needs to optimize many parameters one by one so as to delay the convergence speed of the algorithm.…”

Section: Nonhomogeneous Linear Polygonal T‐s Fuzzy Systemmentioning

confidence: 99%

“…In 2016, Suo and Wang 21 established the polygonal Mamdnai fuzzy system based on the ordered representation of a polygonal fuzzy number, and designed the FA for the fuzzy system through the fitness function and the fluorescein decision radius, but the Takagi–Sugeno (T‐S) fuzzy system did not involve in the research of intelligent algorithm. In recent years, some intelligent algorithms about the polygonal fuzzy neural network have been proposed one after another 22–25 . These results have important theoretical significance for further expanding the wide application of intelligent algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Design and optimization of TS firefly algorithm based on the nonhomogeneous linear polygonal T‐S fuzzy system

2020

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Section: Nonhomogeneous Linear Polygonal T‐s Fuzzy Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and optimization of TS firefly algorithm based on the nonhomogeneous linear polygonal T‐S fuzzy system

2020

Self Cite

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“…In 2016, Li and Li [23] constructed a single input single output PFNN by means of equidistant partition of domain and interpolation function. In 2018, Wang and Suo [24] designed the connection weights and threshold parameters, and proposed the isolation layered algorithm of the multiple input multiple output PFNN. See [25].…”

Section: Introductionmentioning

confidence: 99%

Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

Sun

Wang

2022

Complex Intell. Syst.

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As a special case of general fuzzy numbers, the polygonal fuzzy number can describe a fuzzy object by means of an ordered representation of finite real numbers. Different from general fuzzy numbers, the polygonal fuzzy numbers overcome the shortcoming of complex operations based on Zadeh’s traditional expansion principle, and can maintain the closeness of arithmetic operation. Hence, it is feasible to use a polygonal fuzzy number to approximate a general fuzzy number. First, an extension theorem of continuous functions on a real compact set is given according to open set construction theorem. Then using Weierstrass approximation theorem and ordered representation of the polygonal fuzzy numbers, the convergence of a single hidden layer feedforward polygonal fuzzy neural network is proved. Secondly, the gradient vector of the approximation error function and the optimization parameter vector of the network are given by using the ordered representation of polygonal fuzzy numbers, and then the gradient descent algorithm is used to train the optimal parameters of the polygonal fuzzy neural network iteratively. Finally, two simulation examples are given to verify the approximation ability of the network. Simulation result shows that the proposed network and the gradient descent algorithm are effective, and the single hidden layer feedforward network have good abilities in learning and generalization.

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“…Wang and Gao [9] gave a three-layer polygonal fuzzy neural network (PFNN) model based on n-polygonal fuzzy numbers. Wang and Suo [10] constructed the multi-input multi-output (MIMO) polygonal fuzzy neural network model.…”

Section: Introductionmentioning

confidence: 99%

n-Intuitionistic Polygonal Fuzzy Aggregation Operators and Their Application to Multi-Attribute Decision Making

Geng

2020

IEEE Access

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The n-intuitionistic polygonal fuzzy set (n-IPFS), combined by the intuitionistic fuzzy and polygonal fuzzy sets, is an extended form of the triangular intuitionistic fuzzy set (TIFS) and the trapezoidal intuitionistic fuzzy set (TrIFS). The aim of this paper is to develop some new aggregation operators for n-IPFSs and apply them to multi-attribute group decision making (MAGDM) problems. First, the operational properties and the score function of n-IPFSs are defined. Then, three kinds of n-intuitionistic polygonal fuzzy aggregation operators are investigated including n-intuitionistic polygonal fuzzy weighted averaging (n-IPFWA) operator, n-intuitionistic polygonal fuzzy ordered weighted averaging (n-IPFOWA) operator and n-intuitionistic polygonal fuzzy hybrid aggregation (n-IPFHA) operator. Finally, we propose an improved technique for order preference by similarity to an ideal solution (TOPSIS) approach with n-IPFSs and unknown attributes weights. The attributes weights are obtained by combining the entropy weights and the subjective weights, and the entropy weights are calculated based on the score function of n-IPFS. The spatial closeness reflected by the Hamming distance and the grey relationship with the positive/negative solution are both considered in getting the relative closeness degree to rank the alternatives. The example analysis of a location selection is given to verify the practicality and the effectiveness of the proposed approach in this paper. INDEX TERMS n-intuitionistic polygonal fuzzy set, multi-attribute group decision making, aggregation operator, TOPSIS, grey relation analysis

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The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

Cited by 10 publications

References 14 publications

Design and optimization of TS firefly algorithm based on the nonhomogeneous linear polygonal T‐S fuzzy system

Design and optimization of TS firefly algorithm based on the nonhomogeneous linear polygonal T‐S fuzzy system

Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

n-Intuitionistic Polygonal Fuzzy Aggregation Operators and Their Application to Multi-Attribute Decision Making

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