This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information...

This article describes an indirectly encoded evolutionary learning algorithm to train morphological neural networks. The indirect encoding method is an algorithm in which the training of the neural network is done by finding the solution without considering the exact connectivity of the network. Looking for the set of weights and architecture in a reduced search space, this simple, but powerful...

This paper proposes a new partial discharge (PD) pattern recognition method base on the extension theory. First, five types of defect models are well-designed on the base of investigation of many power equipment failures. A commercial PD detector is used to measure the three-dimension (3D) PD patterns, then two fractal features (fractal dimension and lacunarity) and mean discharges of phase win...

Purpose Exposure to various stressors, including psychological, metabolic, and immune, in the perinatal period induces long-lasting effects in physiological function and increase the risk of metabolic disorders in later life. In the present study, sexual maturation and sexual behavior were assessed in prenatally undernourished mature male rats. Methods All the pregnant rats were divided into ...

This paper considers the general problem of function estimation with a modular approach of neural computing. We propose to use functionally independent subnetworks to learn complex functions. Thus, function approximation is decomposed and amounts to estimate different elementary sub-functions rather than the whole function with a single network. This modular decomposition is a way to introduce ...

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

Modular Neural Networks Qiangfu Zhao The University of Aizu Aizu-Wakamatsu, Fukushima 965-8580, Japan Email: [email protected] Abstract In the last decade, a number of neural network models have been proposed in the literature. Some of them have been successfully incorporated in di erent intelligent information processing systems. Among these models, a group of most successful ones are the m...

In this work, we propose a modular combination of two popular applications of neural networks to large-vocabulary continuous speech recognition. First, a deep neural network is trained to extract bottleneck features from frames of mel scale filterbank coefficients. In a similar way as is usually done for GMM/HMM systems, this network is then applied as a nonlinear discriminative feature-space t...

In this article, a modular neurocontroller is presented. It has the capability to generate a reactive behavior of walking machines. The neurocontroller is formed on the basis of a modular structure. It consists of the three different functionality modules: neural preprocessing, a neural oscillator network and velocity regulating networks. Neural preprocessing is for sensory signal processing. T...