Synchronization is very important for the efficient processing and transmission of information across the nervous system. Synchronization processes are ubiquitous in nature and play a very important role in many areas. As a special neural networks, fuzzy neural networks have an automatic adaption procedure and attract many attentions to their dynamical behaviors. In this paper, delayed fuzzy hi...

In this paper, by employing a new Lyapunov functional and an elementary inequality analysis technique, some sufficient conditions are derived to ensure the existence and uniqueness of periodic oscillatory solution for fuzzy bi-directional memory (BAM) neural networks with time-varying delays, and all other solutions of the fuzzy BAM neural networks converge the uniqueness periodic solution. The...

The extraction of easily interpretable knowledge from the large amount of data measured in experiments is well desirable. This paper proposes a method to achieve this. A fuzzy rule system isjirst generated and optimized using evolution strategies. This fuzzy system is then converted to an RBF neural network to reJine the obtained knowledge. In order to extract understandable fuzzy rules from th...

-Intrusion Detection System is used to detect the unwanted activities over the network and to design IDS soft computing techniques are used. This paper describes the role of Artificial Neural Network, Fuzzy Logic and Genetic Algorithm in Intrusion Detection System. The artificial neural network learning algorithms, data retrieval using fuzzy logic under uncertainty and power of Genetic algorith...

Abstract: In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence betw...

The synergy of the two paradigms, neural network and fuzzy inference system, has given rise to rapidly emerging filed, neuro-fuzzy systems. Evolving neuro-fuzzy systems are intended to use online learning to extract knowledge from data and perform a high-level adaptation of the network structure. We explore the potential of evolving neuro-fuzzy systems in reinforcement learning (RL) application...

A supervisory hybrid system may be modeled from two levels: logic level (upper) and continuous level (lower). In this paper, adaptive fuzzy Petri nets and neural networks are combined together for supervisory hybrid system modeling. Adaptive Fuzzy Petri Net is adopted to model the supervisory logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples ar...

The paper presents a general framework of connectionistbased, intelligent decision support systems and its realisation with the use of fuzzy neural networks FuNNs and evolving fuzzy neural networks EFuNNs. FuNNs and EFuNNs facilitate learning from data, fuzzy rule insertion, rule extraction, and adaptation. Several applications of this framework on real problems are presented as case studies, t...

Fuzzy neural networks have several features that make them well suited to a wide range þÿ o l knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the fonn of semanticallymeaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under co...