This paper discusses the generalization capability of neural networks based on various fuzzy operators introduced earlier by the authors as Fuzzy Flip-Flop based Neural Networks (FNNs), in comparison with standard (e.g. tansig function based, MATLAB Neural Network Toolbox type) networks in the frame of simple function approximation problems. Various fuzzy neurons, one of them based on a pair of...

A novel neuro-based method is introduced to solve the laminar boundary layer and the turbulent free jet equations. The proposed method is based on cellular neural networks, CNNs, which are recently applied widely to solve partial differential equations. The effectiveness of the method is illustrated through some examples.

A novel neuro-based method is introduced to solve the laminar boundary layer and the turbulent free jet equations. The proposed method is based on cellular neural networks, CNNs, which are recently applied widely to solve partial differential equations. The effectiveness of the method is illustrated through some examples.

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization (PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to o...

This paper proposes a novel clustering algorithm for the structure learning of fuzzy neural networks. Our clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes at every training sample. Compared with the classical clustering algorithms, the new algorithm can on-line partition the input data, pointwise update the clusters, and self-...

The paper describes an application of evolvable fuzzy neural networks for artificial creativity in linguistics. The task of the creation of an English vocabulary was resolved with neural networks which have an evolvable architecture with learning capabilities as well as a fuzzy connectionist structure. The paper features a form of artificial creativity which creates words on its own using genet...

The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization problems. In the proposed GA, values in the genotypes are not real numbers but fuzzy numbers. Evolutionary processes in GA are extended so that GA can handle genotype instances with fuzzy numbers. The proposed method is applied to evolving neural networks with fuzzy weights and biases. Experimental ...

Abstract In this article we introduce the p -adic cellular neural networks which are mathematical generalizations of classical (CNNs) introduced by Chua and Yang. The new have infinitely many cells organized hierarchically in rooted trees, also they hidden layers. Intuitively, CNNs occur as limits large hierarchical discrete CNNs. More precisely, can be very well approximated Mathematically spe...