Neural-network-based fuzzy logic control and decision system

Connection admission control of ATM network using integrated MLP and fuzzy controllers

“…Unlike neural network, the control algorithm in a fuzzy controller is a knowledge-based algorithm, described by the methods of fuzzy logic [9]. This controller bases its decisions on inputs in the form of linguistic variables derived from membership functions (MFs).…”

GA-Based Learning Algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks

“…First stage is initializing the membership functions of both input and output variables by determining their centres and widths. To perform this stage, we have employed a self-organizing algorithm [6] as in other works [2,5,16]. A proposed GA based learning algorithm is performed in the second stage to identify the fuzzy rules that are supported by the set of training data.…”

“…In this paper we have used GA to make use of the known membership function to identify the fuzzy rules using similar fuzzy neural network structure discussed in [2]. The proposed genetic algorithm differs form existing algorithms such as [5,6], in terms of being simple, fast and flexible to control the process of identification of the fuzzy rules based on the error level.…”

“…Although this approach converges faster during training and performs better, it is rather subjective since linguistic information from experts may vary from person to person, and from time to time. The second category of approaches uses unsupervised learning algorithms to identify fuzzy rules in the fuzzy neural networks prior to the application of neural network techniques to adjust the rules [5,6]. In this approach the training data is the only source of information so it must be representative, otherwise, the derived fuzzy rules will be ill defined.…”

Power quality event classification: an overview and key issues

“…Adaptive neuro-fuzzy system (ANFS) is a hybrid system incorporating the learning abilities of ANN and excellent knowledge representation and inference capabilities of fuzzy logic (Jang, 1993;Jang et al, 1995;Lin et al, 1991) that have the ability to self modify their membership function to achieve a desired performance. An adaptive network, which subsumes almost all kinds of neural network paradigms, can be adopted to interpret the fuzzy inference system.…”