1.1 A Transputer Mobile Robotics System Mobile robots have received a considerable attention from early research community, from (A. Benmounah, 1991), (Maamri, 1991), (Meystel, 1991) up to this instant (Hegazy, et. al, 2004), and (Pennacchio, et. al., 2005). A fuzzy or neural control Transputer based control mobile robots has received, rather, little attention. A number of, are (Welgarz,1994), ...

In this paper, evolutionary and dynamic programming based reinforcement learning techniques are combined to form an unsupervised learning scheme for designing autonomous optimal fuzzy logic control systems. A messy genetic algorithm, and an advantage learning scheme are first compared as reinforcement learning paradigms. The messy genetic algorithm enables flexible coding of a fuzzy structure f...

A self-tuning neurofuzzy integrating controller is derived in this paper for offset eliminating purpose. CARIMA plant model is used and the control law produces integral control terms in a natural way. Neurofuzzy networks are chosen to implement the direct self-tuning nonlinear integrating controller. The performance of the self-tuning integrating neurofuzzy controller is illustrated by example...

A Fuzzy logic system has been shown to be able to arbitrarily approximate any nonlinear function and has been successfully applied to system modelling. The functional rule fuzzy system enables the input-output relation of the fuzzy logic system to be analysed. B-spline basis functions have many desirable numerical properties and as such can be used as membership functions of fuzzy system. This ...

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...

In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional l...

In this paper, a neuro-fuzzy system identification using measured input and output data are carried out. A model-free learning from “examples” methodology is developed to train a neuro-fuzzy model of a smallsize helicopter. The helicopter model is obtained and tuned using training data gathered while a teacher operates the helicopter. Behavior-based model architecture is used, with each behavio...

Artificial intelligence techniques such as neural networks and fuzzy logic have been widely used in fault detection and diagnosis. Combining these two techniques, referred to as neurofuzzy networks, provides a powerful tool for modelling. B-spline neurofuzzy networks are used to model the residuals. The weights of the networks are trained online using recursive least squares method. Fuzzy rules...

In this paper a new systematic modelling approach using Granular Computing (GrC) and Neurofuzzy modelling is presented. In this study a GrC algorithm is used to extract relational information and data characteristics out of the initial database. The extracted knowledge is then translated into a linguistic rule-base of a fuzzy system. This rule-base is finally realised via a Neurofuzzy modelling...