Reinforcement learning is one of the most important learning methods for intelligent robots working in unknown/uncertain environments. Multi-dimensional fuzzy Q-learning, an extension of the Q-learning method, has been proposed in this study. The proposed method has been applied for an intelligent robot working in a dynamic environment. The rewards from the evaluation functions and the fuzzy Q-...

This paper introduces the Petri Net Radial Basis Function Perceptron (PNRBFP), a modified Petri Net that exhibits behavior equivalent to that of a typical radial basis function Perceptron when used in neural networking applications under certain domain restrictions. The PNRBFP makes use of modified transitions to perform basis function calculations and 'fuzzy' style tokens to transport values o...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

uml is known as one of the most common methods in software engineering.since this language is semi-formal, many researches and efforts have beenperformed to transform this language into formal methods including petri nets.thus, the operation of verification and validation of the qualitative and nonfunctionalparameters could be achieved with more ability. since the majority of thereal world info...

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic -means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fu...

A novel scheme for developing, at low computational cost, neural-fuzzy classifiers based on large-scale, model-based exemplars is outlined. The new method extends the approach that Bezdek applied to train a neural net (NN) Sobel edge classifier by training the NN on the complete population of 3x3 binary image prototypes scored to fuzzy values by a classical operator. We first show that, replaci...

Navigation of multiple mobile robots using neuro-fuzzy controller has been discussed in this paper. In neuro-fuzzy controller the output from the neural network is fed as an input to fuzzy controller and the final outputs from the fuzzy controller are used for motion control of robots. The inputs to the neural network are obtained from the robot sensors (such as left, front, right obstacle dist...

This is a description of a simple system that is able of performing abductive reasoning over fuzzy data using a back-propagation neural net for the hypothesis generation process. I will rst outline and exemplify the notion of abduction as a process of building hypotheses on the basis of a given set of data, evaluating them to nd the best hypothesis and give explanation for the made selection. I...