In this paper, first a new algorithm for pole assignment of closed-loop multi-variable controllable systems in a prescribed region of the z-plane is presented. Then, robust state feedback controllers are designed by implementing a neural fuzzy system for the placement of closed-loop poles of a controllable system in a prescribed region in the left-hand side of z-plane. A new method based on the...

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...

Nonlinear dynamic ball balancing beam has been successfully controlled by applying conventional methods, neural networks, and fuzzy logic respectively. Conventional methods necessitate strong mathematical and control background to derive equations. Neural networks learn to balance a ball, but the ball never settles down due to the fact that \discrete resolution of the boxes representation" was ...

Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...

By combining the fuzzy theory and neural network technology, a fuzzy neural network (FNN) is proposed in this paper, whose learning algorithms are developed by steep algorithm. The excitation system model based on FNN is also derived in this paper, which can be used for on-line and off-line analysis and control respectively. The simulation results demonstrate that the FNN models can give precis...

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

The paper compares the use of neural network and neurofuzzy approaches in diagnosis of endogenous intoxication syndrome. The comparison was carried out on real patient data. Data preprocessing, neural network design and experiments are reported. A fuzzy neural network and results of experiments with it are presented. The advantages of fuzzy neural network are shown.

In our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memet...

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...