today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

in this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.the proposed method combines the fuzzy c-means clustering method, a recurrent functional neural fuzzy network (rfnfn), and a modified differential evolution.the proposed rfnfn is based on the two backlight factors that can accurately detect the compensat...

This paper explores the use of recurrent neural networks for sub-optimal detection in code division multiple access systems. Research has shown that detectors based on the Hopfield recurrent neural network suffer from localized optimization. The basic Hopfield model is reviewed and we illustrate its use as a multiuser receiver. We investigate the use of stochastic methods to achieve a global mi...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

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...

This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are forme...

The RPROP algorithm was originally developed in [5] for static networks and constitutes one of the best performing first order learning methods for neural networks [6]. However, in RPROP the problem of poor convergence to local minima, faced by all gradient descent-based methods, is not fully eliminated. Hence, in an attempt to alleviate this drawback, a combination of RPROP with the global sea...