Hierarchical fuzzy neural networks can use less rules to model nonlinear system with high accuracy. But the normal training method for hierarchical fuzzy neural networks is very complex. In this paper we modify the backpropagation approach and employ a time-varying learning nte that is determined from input-output data and model stnicture. Stable learning algorithms for the premise and the cons...

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path for an autonomous mobile robot in a dynamic environment containing moving and static obstacles using neural network and fuzzy logic with genetic algorithm. The mobile robot selects a...

This paper proposes a hardware model that provides new fire detection and control mechanism with the interface of artificial neural network and fuzzy logic. This work is based on the integration of hardware module and implementation of artificial neural fuzzy inference system (ANFIS). The hardware consists of temperature sensor, smoke sensor, flame detector and a microcontroller unit. The senso...

This paper pro oses a neural fuzzy connection admission control NFCAC) scheme, which combines of the neural-net, t o solve the connection admission control (CAC) problems in ATM networks. Recently, fuzzy logic systems have been successfully applied t o deal with the traffic control related probleims and provided a robust mathematical framework for dealing with "real-world" imprecision; multi-la...

Computer programs are playing key role in Medicine not only in Medical Information Systems but also in Medical Diagnosis and Surgery. Artificial Intelligence in Medicine particularly Expert Systems are used for diagnosis and robotics are used in surgery. The Robotics will assist the surgeon in Surgery. The Medical Expert Systems will assist the physician in Diagnosis. The information available ...

Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters can be carried out by neural networks, which are designed to learn from training data, but which a...

Development of a neural-fuzzy model for an operational hydrocyclone is reported in this paper. The model integrates the benefits of the Artijkial Neural Network (ANN) and the fuzzy-logic techniques. It preserves the generalisation capability of an ANN while expressing the final model in fuzzy rules. These rules can be modiJied and examined by the user. This will in turn control the interpretati...

Fault diagnosis of pneumatic system of automatic production line is studied in this paper. A fuzzy neural network fault diagnosis expert system and pneumatic circuit fault diagnosis instrument are designed. The experiment device is built as well. In the end some experiments are done, whose results show that the expert system using fuzzy neural network can diagnose fast and truly fault of pneuma...

The Hand Sign Classification (HSC) system classifies hand movement data into Australian Sign Language (AUSLAN) signs. It is built as a fuzzy expert system with an adaptive engine that trains the system to handle variations in the movement data, or to adapt to differences amongst signers. Adaptive fuzzy systems are often compared with neural networks in their adaptability, but unlike neural netw...

The next paper presents the development of a non-lineal dynamic system modelling using the combination of neural networks with fuzzy logic. The first approximation method used is ANFIS. With this method, the most significant rules were selected and slightly modified to obtain a significantly better result. This procedure was applied on a case study where an environmental system was modelled. Ke...