MIXTURE BASIS FUNCTION APPROXIMATION AND NEURAL NETWORK EMBEDDING CONTROL FOR NONLINEAR UNCERTAIN SYSTEMS WITH DISTURBANCES

Mixture Basis Function Approximation and Neural Network Embedding Control for Nonlinear Uncertain Systems with Disturbances

A neural network embedding learning control scheme is proposed in this paper, which addresses 5 Piece LAF Reclining Sectional the performance optimization problem of a class of nonlinear system with unknown dynamics and disturbance by combining with a novel nonlinear function approximator and an improved disturbance observer (DOB).We investigated a

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Crisis Development and its management

The paper deals with the theoretical concept of crisis.The crisis is an integral and, in essence, regularly recurring part of any human activity.We are facing a crisis in areas such as the economy, politics, the military, civilization, as well as in our private, human lives.The paper analyzes in detail the dimensions and characteristics of the cris

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Coastal solid waste prediction by applying machine learning approaches (Case study: Noor, Mazandaran Province, Iran)

Nowadays, intelligent systems are used as innovative tools in different environmental issues.However, the prediction of short-term waste, unlike the long-term scale, is less developed due to more uncertainties and the difficulty in determining measurable independent parameters.In this study, two types of artificial neural networks (MLP and RBF) and

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Reciprocal innovation: a new approach to equitable and mutually beneficial global health research and partnerships

Background: The Indiana University Center for Global Health coined the term reciprocal innovation to describe bidirectional, mutually beneficial research and translational research within long-term global health partnerships.Inspired by a 30-year global health partnership between Indiana University (Indianapolis, IN, USA) and Moi University College

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