New Model Of Neural Network Research Articles

A simple neural network solar tracker for optimizing conversion efficiency in off-grid solar generators

A new model of neural network and a new type of neural controller are proposed, aiming to reduce cost and complexity without sacrificing efficiency of traditional, more complex neural net-based solar trackers. The solution is derived from Mark Tilden’s neural and nervous networks, using a biologic analogy to seamlessly integrate sensors, artificial neurons and effectors in a single, efficient device. Testing is in progress at the time of the elaboration of this paper but available and relevant preliminary results are shown. The project aims to develop a small pilot tracker – based solar plant for testing purposes and to develop a useable technology for the ever-growing demand for green power.

Exponential synchronization of memristive Hindmarsh–Rose neural networks

A new model of neural networks in terms of the memristive Hindmarsh–Rose equations is proposed. Globally dissipative dynamics is shown with absorbing sets in the state spaces. Through sharp and uniform grouping estimates and by leverage of integral and interpolation inequalities tackling the linear network coupling against the memristive nonlinearity, it is proved that exponential synchronization at a uniform convergence rate occurs when the coupling strengths satisfy the threshold conditions which are quantitatively expressed by the parameters.

Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions

Application of neural network technologies is becoming more extensive every year, especially in the economic field. New methods are being discovered, new models of neural networks are being created. Currently, the main application of neural networks in the economy is the prediction of markets, optimization of commodity-money flows, analysis and generalization of various social surveys, prediction of the dynamics of political ratings, optimization of the production process, comprehensive diagnostics of product quality, etc. Utilisation of the hydraulic equipment makes it possible to design systems of automatic operation in the conditions, where minimal human participation and maximum speed of response are required. It is possible to state that such conditions exist in the regions with the permanent access of repair teams or technicians, who perform monitoring of the complex technical objects. Therefore, it is necessary to develop automated systems of operation and monitoring of various equipment components, which are intended for operation in the complicated technical conditions. Authors of this article have selected the hydraulic equipment as the object under investigation due to the fact that it is widely distributed equipment, as well as due to possibilities of this equipment to function or to be adapted for operation in practically any environmental conditions. At the same time, quantity of the state-of-the-art equipment, which is used, as well as complexity of this equipment increase very quickly, therefore process of making decision concerning utilisation of this equipment must be made very quickly. Authors analyse the sphere of automation, where utilisation of a human decision is required. The novelty of this article is connected with the assumption that further direction of operation of such equipment in the complicated technical conditions must be implemented in the sphere of guessing of the user’s actions. Authors review neural networks as the toolkit, and they believe that these networks can make decisions in the proactive mode, practically without participation of a user. This article includes description of the model, which can be used as the basis for the system, which is planned to design. In addition, methodological toolkit for assessment of efficiency of this model is proposed. Keywords: equipment, neural network, complicated technical conditions, functioning, model, economic efficiency DOI: 10.25165/j.ijabe.20201301.3965 Citation: Togizbayeva B B, Sazambayeva B T, Karazhanov A A, Kenesbek A B, Cocosila M. Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions. Int J Agric & Biol Eng, 2020; 13(1): 11–19.

Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions

Application of neural network technologies is becoming more extensive every year, especially in the economic field. New methods are being discovered, new models of neural networks are being created. Currently, the main application of neural networks in the economy is the prediction of markets, optimization of commodity-money flows, analysis and generalization of various social surveys, prediction of the dynamics of political ratings, optimization of the production process, comprehensive diagnostics of product quality, etc. Utilisation of the hydraulic equipment makes it possible to design systems of automatic operation in the conditions, where minimal human participation and maximum speed of response are required. It is possible to state that such conditions exist in the regions with the permanent access of repair teams or technicians, who perform monitoring of the complex technical objects. Therefore, it is necessary to develop automated systems of operation and monitoring of various equipment components, which are intended for operation in the complicated technical conditions. Authors of this article have selected the hydraulic equipment as the object under investigation due to the fact that it is widely distributed equipment, as well as due to possibilities of this equipment to function or to be adapted for operation in practically any environmental conditions. At the same time, quantity of the state-of-the-art equipment, which is used, as well as complexity of this equipment increase very quickly, therefore process of making decision concerning utilisation of this equipment must be made very quickly. Authors analyse the sphere of automation, where utilisation of a human decision is required. The novelty of this article is connected with the assumption that further direction of operation of such equipment in the complicated technical conditions must be implemented in the sphere of guessing of the user’s actions. Authors review neural networks as the toolkit, and they believe that these networks can make decisions in the proactive mode, practically without participation of a user. This article includes description of the model, which can be used as the basis for the system, which is planned to design. In addition, methodological toolkit for assessment of efficiency of this model is proposed. Keywords: equipment, neural network, complicated technical conditions, functioning, model, economic efficiency DOI: 10.25165/j.ijabe.20201301.3965 Citation: Togizbayeva B B, Sazambayeva B T, Karazhanov A A, Kenesbek A B, Cocosila M. Simulation of operation of neural network with purpose of utilisation of hydraulic actuators in complicated technical conditions. Int J Agric & Biol Eng, 2020; 13(1): 11–19.

A review of various semi-supervised learning models with a deep learning and memory approach

Based on data types, four learning methods have been presented to extract patterns from data: supervised, semi-supervised, unsupervised, and reinforcement. Regarding machine learning, labeled data are very hard to access, although unlabeled data are usually collected and accessed easily. On the other hand, in most projects, most of the data are unlabeled but some data are labeled. Therefore, semi-supervised learning is more practical and useful for solving most of the problems. Different semi-supervised learning models have been introduced such as iterative learning (self-training), generative models, graph-based methods, and vector-based techniques. In addition, deep neural networks are used to extract data features using a multilayer model. Various models of this method have been presented to deal with semi-supervised data such as deep generative, virtual adversarial, and Ladder models. In semi-supervised learning, labeled data can contribute significantly to accurate pattern extraction. Thus, they can result in better convergence by having greater effects on models. The aim of this paper was to analyze the available models of semi-supervised learning with an approach to deep learning. A research solution for future studies is to benefit from memory to increase such an effect. Memory-based neural networks are new models of neural networks which can be used in this area.

Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays

In this paper, we address a new model of neural networks related to the impulsive phenomena which is called state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays. We investigate sufficient conditions on the existence and uniqueness of exponentially stable anti-periodic solution for these neural networks by employing method of coincide degree theory and an appropriate Lyapunov function. Moreover, we present an illustrative example to show the effectiveness and feasibility of the obtained theoretical results.

FPGA implementations of scale-invariant models of neural networks

Integrated circuit implementations of new models of neural networks with scale-invariant properties are presented. The specifics of such models are necessary in analysis of discrete mappings containing fractional power. We suggest an algorithm for increasing the power of a physical value by using a field-programmable gate array (FPGA). Comparisons between FPGA implementations and numerical results are demonstrated.

Almost periodic solutions of impulsive neural networks at non-prescribed moments of time

In this paper, we address a new model of neural networks related to the discontinuity phenomena which is called impulsive recurrent neural networks with variable moments of time. Sufficient conditions for existence and uniqueness of exponentially stable almost periodic solution are investigated. An example is given to illustrate our theoretical results.

Dynamics and stability of generalized cellular nonlinear network model

The aim of this paper is the definition of a new model of neural network, called generalized cellular nonlinear network, that covers architectures and dynamics of the well known and widely used classes of feedforward neural networks and cellular neural networks. We show how cellular neural networks and feedforward neural networks can be derived from the general model: moreover we prove a theorem of existence and uniqueness for the solution of the system that describes the generalized cellular nonlinear network dynamics. These results are obtained using the method of Lyapunov’s finite majorizing equations that also represents a new approach in studying the stability of cellular neural networks.

Chaotic Brownian network

A system of many Brownian particles interacting via the chaotic force is proposed as a new model of neural network, which is called the chaotic Brownian network. The network is applied to the associative memory problem. The coupling constants are determined by the Hebb rule. It is shown that some neuron pairs, which are specified by the stored patterns, exhibit the synchronized motions with the same sign or with the opposite sign. The relation between the synchronized motion and the chaotic wandering motion is discussed. The network is shown to retrieve the stored patterns or the reverse ones systematically and very quickly. It is shown that the network can store and retrieve the number of patterns comparable to the number of neurons.

A neural network based quality control system for steel strip manufacturing

A real time quality control system for flat products, applied to steel strip manufacturing, is introduced. The system is able to perform this task when the product runs at its maximum speed (15 m/s). The outlines of the overall system are explained, paying special attention to the neural classifier. A new model of neural network has been developed in order to implement this subsystem. Some benchmark results are resembled in order to ease the understanding of the model.