Fuzzy Neural Networks
نویسندگان
چکیده
This paper comparator networks a well-known model of parallel computation. This model is used extensively for keys arrangement tasks such as sorting and selection. This work investigates several aspects of comparator networks. It starts with presenting handy tools for analysis of comparator networks in the form of conclusive sets non-binary vectors that verify a specific functionality. The 0-1 principle introduced by Knuth states that a comparator network is a sorting network if and only if it sorts all binary inputs. Hence, it points out a certain binary conclusive set. We compare these two models by considering several 0-1 -like principles and show that the min-max model is the ‘strongest’ model of computation which obeys our principles. That is, if a function is computable in a model of computation in which any of these principles holds, a min-max network can compute this function.
منابع مشابه
INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
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 ...
متن کاملGyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...
متن کاملFINITE-TIME PASSIVITY OF DISCRETE-TIME T-S FUZZY NEURAL NETWORKS WITH TIME-VARYING DELAYS
This paper focuses on the problem of finite-time boundedness and finite-time passivity of discrete-time T-S fuzzy neural networks with time-varying delays. A suitable Lyapunov--Krasovskii functional(LKF) is established to derive sufficient condition for finite-time passivity of discrete-time T-S fuzzy neural networks. The dynamical system is transformed into a T-S fuzzy model with uncertain par...
متن کاملRobust stability of stochastic fuzzy impulsive recurrent neural networks with\ time-varying delays
In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...
متن کاملAn Approach of Artificial Neural Networks Modeling Based on Fuzzy Regression for Forecasting Purposes
In this paper, a new approach of modeling for Artificial Neural Networks (ANNs) models based on the concepts of fuzzy regression is proposed. For this purpose, we reformulated ANN model as a fuzzy nonlinear regression model while it has advantages of both fuzzy regression and ANN models. Hence, it can be applied to uncertain, ambiguous, or complex environments due to its flexibility for forecas...
متن کاملAN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...
متن کامل