USE OF NEURAL NETWORKS FOR SOLVING PROBLEMS OF NON-CONNECTING PROBLEMS AND SOLUTION OF COMPOSITE COMPARTMENT EQUATIONS OF ELECTRIC TRANSPORT OPERATION
The use of neural networks to solve the problems of insolubility and the solution of complex computational equations becomes a common practice in academic circles and industry. It has been shown that, despite the complexity, these problems can be formulated as a set of equations, and the key is to find zeros from them. Zero Neural Networks (ZNNs), as a class of neural networks specially designed to find zeros of equations, have played an indispensable role in online decision-changing problems over time in recent years, and many fruitful research results have been documented in literature. The purpose of this article is to provide a comprehensive overview of ZNN studies, including ZNN continuous time and discrete time models for solving various problems, and their application in motion planning and superfluous manipulator management, chaotic system tracking, or even population control in mathematical biological sciences. Considering the fact that real-time performance is in demand for time-varying problems in practice, analysis of the stability and convergence of various ZNN models with continuous time is considered in a unified form in detail. In the case of solving the problems of discrete time, procedures are summarized for how to discriminate a continuous ZNN model and methods for obtaining an accuracy decision.
Approaches based on the neural network to address various nodal tasks have attracted considerable attention in many areas. For example, an adaptive fuzzy controller based on a neural network is constructed for a class of nonlinear systems with discrete time with a dead zone with discrete time in. An applied decentralized circuit, based on a neural network, is presented for multiple nonlinear input and multiple output systems (MIMO) using the methods of the reverse step in. Such a scheme guarantees a uniform limiting limit of all signals in a closed system relative to the average square. In order to overcome the structural complexity of the nonlinear feedback structure, uses the method of dividing variables for the decomposition of unknown functions of all state variables into the sum of smooth functions of each dynamic error.
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