Multilayer Neural Network With Multi-valued Neurons Research Articles

Failure Prevention in DC–DC Converters: Theoretical Approach and Experimental Application on a Zeta Converter

In this article, we propose a monitoring procedure based on a multilayer neural network with multivalued neurons (MLMVN) capable of preventing catastrophic failures of dc–dc converters. The neural classifier allows both the detection of any malfunction and its localization. Thanks to the low computational complexity, the proposed method operates online, estimating the deviations of the passive components from their nominal values: this allows control strategies to be promptly adopted and operation of the dc–dc converter to be kept in high efficiency and reliability conditions. Since measuring the voltage and current on each component increases the complexity of the system, a testability analysis is proposed with the aim of identifying the minimum number of measurements needed to distinguish the classes of failure. To make the testability phase easier and more intuitive, a graphical representation is proposed. As a case study, prognostic analysis has been applied to prevent catastrophic failures in a synchronous Zeta converter. Several fault conditions have been analyzed through simulations and experimental tests. The obtained results confirm the ability of the proposed method to prevent failures and, also, show that the application of MLMVN results in a better performance than classic solutions available in the literature, such as support vector machine.

Intelligent Image Filtering using Multilayer Neural Network with Multi-Valued Neurons

Image filtering, regardless of whether it is denoising (low pass filtering) or edge detection (high pass filtering) can be considered as a machine learning problem. In fact, filtering is a process of approximation of a desirable result. A filter is considered good, if it approaches this ideal result better than other filters. But any machine learning problem should be considered from exactly the same standpoint. In this paper, we suggest to use a multilayer neural network with multi-valued neurons (MLMVN) as an intelligent image filter. After MLMVN is trained using a number of n x n patches from different images to obtain a clean patch from a noisy one, it can be used as an intelligent filter. It is shown that this filter is robust, since it performs well on different images, which did not participate in the learning process. It terms of PSNR, this approach shows results comparable with other filters commonly recognized as good. A specific advantage of the presented approach is its ability to preserve small details carefully.

Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production

In this paper, we discuss the long-term time series forecasting using a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). This is a complex-valued neural network with a derivative-free backpropagation learning algorithm. We evaluate the proposed approach using a real-world data set describing the dynamic behavior of an oilfield asset located in the coastal swamps of the Gulf of Mexico. We show that MLMVN can be efficiently applied to univariate and multivariate one-step- and multi-step ahead prediction of reservoir dynamics. This paper is not only intended for proposing to use a complex-valued neural network for forecasting, but to deeper study some important aspects of the application of ANN models to time series forecasting that could be of the particular interest for pattern recognition community.

MLMVN With Soft Margins Learning

In this paper, we consider a modified error-correction learning rule for the multilayer neural network with multivalued neurons (MLMVN). This modification is based on the soft margins technique, which leads to the minimization of the distance between a cluster center and the learning samples belonging to this cluster. MLMVN has a derivative-free learning algorithm based on the error-correction learning rule and demonstrate a higher functionality and better generalization capability than a number of other machine learning techniques. The discrete k-valued multivalued neuron activation function divides a complex plane into k equal sectors. For more efficient and reliable solving of classification problems it is possible to modify the MLMVN learning algorithm in such a way that learning samples belonging to different classes (clusters) will be located as close as possible to the bisector of a desired sector (the cluster center) and as far as possible from each other, respectively. Such a modification based on the soft margins learning technique is considered in this paper. This modified learning algorithm improves the generalization capability of MLMVN when solving classification problems.