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Chronic kidney failure (chronic kidney disease ‘CKD’) is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage...
In the process of establishing evaluation index system of physical education, the traditional methods setting weights for each indicator mainly include analytic hierarchy process, fuzzy comprehensive evaluation method, and Delphi method, etc. These methods mostly rely on experience, which is strongly influenced by artificial factors and cannot be avoided. Because artificial neural network model has...
In the present work a Cuckoo Search (CS) trained Neural Network (NN) or NN-CS based model has been proposed to detect Chronic Kidney Disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences...
The deep learning of neural network works on vision recognition and classification tasks briskly, and it can extract great features of an image for classification. Recently, many approaches have studied the visual tracking in two-ways with these characteristics. First, they can regard tracking problem as classifying each video and frame by learning all dataset. Second, use the deep neural network...
As an advanced artificial intelligence technology, error back-propagation (BP) neural network algorithm has been widely applied to electronics, communications, automation and other fields. However, traditional BP neural network algorithm has the disadvantages, such as inclination to stick into local optima, and slow convergence, which exert a great impact on the processing performance, and also limit...
The paper approaches the problem of modeling the microwave heating process using Neural Networks. The Neural Network was trained using Matlab and Comsol Multiphysics software. Numerical simulations were made in Comsol Multiphysics, obtaining the necessary input and output data to train the Neural Network. The training was made using Adaptive Neural Network tool from Matlab software.
This paper presents a modeling technique of sequential batch reactor (SBR) for aerobic granular sludge (AGS) using artificial neural network (ANN). A SBR fed with synthetic wastewater was operated at high temperature of 50˚C to study the formation of AGS for simultaneous organics and nutrients removal in 60 days. The feed forward neural network (FFNN) was used to model the nutrients removal process...
Model-based control in the manufacturing of advanced composite materials is one approach to improve the overall quality and performance of the final part. Frequently quantitative nondestructive methods are not available for feedback control, and therefore a predictive controller is required. This study investigates a neural network based control system for the automated thermoplastic composite tow-placement...
A predictor-corrector guidance method that tracks the optimized trajectory of hypersonic reentry glide process is presented. First, aiming at the minimum heating rate problem with multiple constraints, the hp-adaptive pseudospectral method generates optimized trajectory rapidly. Then a BPNN (Back-Propagation neural network) is trained by parameter profiles of optimized trajectory considering different...
A new aerodynamic parameter fitting approach is proposed to avoid online aerodynamic parameter interpolation for advanced flight vehicle trajectory generation, guidance and control. Due to its ability to fit any nonlinear function and simple structure, BP neural network was chosen as the tool to fit the aerodynamic parameters which are the function of Mach number, angle of attack and other variables...
In this paper a Cuckoo Search Algorithm (CSA) based neural network is proposed for noise removal from a signal. A new training function is proposed, which uses Cuckoo Search Algorithm for the training of the network. The trained network is then used to remove noise from a sinusoidal signal contaminated with white Gaussian noise. Various types of random walks and Levy flights are used in the algorithm...
This paper introduces a theoretical new approach for training the adaptive-network-based fuzzy inference system (ANFIS) using Tree Physiology Optimization (TPO). The TPO is a heuristic method based on tree physiology. The method will be applied to nonlinear dynamic system.
The used generalized net will give us a possibility for parallel optimization of multilayer perceptron based on assigned training pairs with momentum backpropagation algorithm. Here we propose optimization with time limit.
Operating system experienced a rise in number of incidents in recent years. Analysis and reemployment of past solution therefore may make a contribution in reducing service interrupt time and minimizing business losses. The training and retaining of human resources is another primary disbursement source for enterprise. Thus, it is of great significance for enterprises to find reasonable solutions...
The flood forecasting is the key to support the right decision making. A method to forecast flood accurately and timely are important. We propose a method based on Radial Basis Function (RBF) neural network which has the important application in flood water level forecasting. The traditional way of training of the neural network may drive the network to converge in local minima instead of global minimum...
Backpropagation algorithm is widely used to solve many real-world problems, using the concept of Multilayer Perceptron. However, main disadvantages of Backpropagation are the convergence rate of it being relatively slow, and it is often trapped in the local minima. To solve this problem, it is found in the literatures, an evolutionary algorithm such as Particle Swarm Optimization algorithm is applied...
In this paper, a density adjustment based Particle swarm optimization algorithm is proposed to solve the problem of premature convergence and global optimal in traditional Particle swarm optimization algorithm. Measure the density of particle swarm by entropy, and update the particle swarm to maintain the swarm diversity, which can also help to improve the ability of global optimization. At the same...
This paper analyses the functions and setting problems of corona ring on composite insulator, use the neural network model to fit the relationship between the structural parameters and the optimization goals, and thus draws the optimal solution of corona ring structure parameters. Finally, it gives optimization parameters of corona ring which can effectively improve the insulator electric field distribution...
A new method based on neural network and genetic algorithm to optimizate the Multiphase Rotodynamic pump is given. Using cubic B-spline surface to parametric the blade profile. Based on the ability of highly nonlinear fitting of BP neural network, the nonlinear relation between the blade parameter and the pump performance parameters is build. Let the trained neural network as a fitness function of...
Input layer weights and hidden layer weights of Neural Network are important to its performance, but selections of these weights depend on experiences and trials, in this paper we present a method to optimize weights of Back-Propagation Neural Network. Input layer weights and hidden layer weights are generated randomly in initialization and optimized with self adaptive parameter Differential Evolution...
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