The paper discusses some biological principles of the human brain that would be useful to implement in intelligent information systems (IS). These principles are used to formulate seven major requirements to the current and the future IS. These requirements are met in a new connectionist architecture called evolving connectionist systems (ECOS). ECOS are designed to facilitate building on-line,...

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...

This study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization methods (PSO-ANFIS), for predicting the equilibrium and kinetics of the adsorption of sulfur and ...

Nowadays intelligent tools such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational proble...

in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performanc...

The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory...

In this paper, the enhancement of vehicle stability and handling is investigated by control of the active geometry suspension system (AGS). This system could be changed through control of suspension mounting point’s position in the perpendicular direction to wishbone therefore the dynamic is alternative and characteristics need to change. For this purpose, suitable controller needs to change...

The objective of this research is to apply intelligent control methodology to improve weld quality. Based on fuzzy logic and artificial neural network theory, a self-learning fuzzy and neural network control scheme has been developed for real-time control of pulsed gas tungsten arc welding (GTAW). Using an industrial TV camera as the sensor, the weld face width of the weld pool, i.e., the feedb...

The design of a conventional power system stabilizer (PSS) based on linearized model cannot guarantee its performance in practical operating, so some intelligent techniques have been used. However, the parameters cannot update online in most of these mehods, thus the performance cannot be further improved. This paper adopts the design method of adaptive neural-fuzzy based power system stabilize...