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In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), where thousands of networks are generated and then evaluated by determining fitness scores on specific tasks. Fitness scores guide the generation of new networks until a target fitness is...
This article proposes a dissolved oxygen prediction model for water quality about aquaculture to solve the problems like low accuracy and poor robustness of traditional prediction methods about water quality based on principal component analysis (PCA), general regression neural network(GRNN), and genetic algorithm (GA). This model can establish nonlinear PH value prediction model of water quality...
This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions...
In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation...
The financial market is complex, evolving and dynamic system, which has an extremely non-linear movement. Thus, investment return prediction represents a significant challenge, especially because of its great diversity, unsteadiness and unstructured data with a high degree of instability and pronounced hidden connections. It is known that accurate prediction of the stock market indexes is very important...
The issue of PM2.5 is becoming a popular atmospheric research hotpot recently. This particular paper evaluates the era reasons as well as influencing factors associated with PM2.5 based on the information associated with PM2.5 in Xing Tai (2014. 01. 01 - 2014. 04. 26), and builds the actual era as well as evolution mode of PM2.5 in Xing Tai by utilizing evolutionary algorithms formula and BP neuron...
Pointing at the problem that the spares consumption quota has been using the experience to develop, which makes spares application random and blind, this paper puts forward to build the reasonable lifeless-repairable spares consumption quota model. Analyze and determine the factors influencing the lifeless-repairable spares consumption, use BP neural network to predict, and use genetic algorithm to...
Several optimization methods can find good solutions for different instances of the Traveling Salesman Problem (TSP). Since there is no method that generates the best solution for all instances, the selection of the most promising method for a given TSP instance is a difficult task. This paper describes a meta-learning-based approach to select optimization methods for the TSP. Multilayer perceptron...
Daily solar radiation prediction is a nonlinear and non-stationary process. It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different...
by using the characteristics of global optimization of GA and local optimization of BP neural network, the calculation accuracy and convergence rate of the traditional BP neural network are improved. Then a special performance forecasting model for shot put based on GA-BP is established. The numerical results show that the mean square errors, the mean absolute value of calculation and prediction are...
Tidal river is often intruded by salt water in dry season. The deterioration of water quality will lower the reliability of drinking water. Raw water systems, which include reservoirs, pump stations and pipelines, were constructed in costal cities. In order to guarantee the safety of drinking water, precise plans of raw water system must be drawn up. So it is necessary to predict salt water intrusion...
The problem of forecast belongs to an input-output nonlinear system in nature. And most of problems which need to be forecasted have a large number of predictors which are relatively correlated. Therefore neural network has unique superiority in dealing with such problems. But when traditional BP (Back-Propagation Network) neural network is used to predict, there are many inadequacies in predictive...
The paper presents a novel adaptive neural-network based nonlinear model predictive control (NMPC) methodology for hybrid systems with mixed inputs. For this purpose an online self-organizing growing and pruning redial basis function (GAP-RBF) neural network is employed to identify the hybrid system using the unscented Kalman filter (UKF) learning algorithm. A receding horizon adaptive NMPC is then...
Forecasting currency exchange rates is an important issue in finance. This topic has received much attention, particularly in econometrics and financial selection of variables that influence forecasts. In this paper, a new forecasting model is constructed: we adopt a Genetic Algorithm (GA) to provide the optimal variables weight and we select the optimal set of variables as the input layer neurons,...
A new combined BP neural network model based on accelerating genetic algorithm is put forward in this paper. On the foundation of traditional BP neural network, this method is given better iteration values improved by accelerating genetic algorithm, thus and increase iteration rate and avoid sinking into local minimum. Then, it is applied to forecast the heat load in a certain area, and compared with...
In this work, artificial neural networks (ANN) based on genetic algorithm (GA) have been developed to predict energy consumption in China. The numbers of neurons in the hidden layer, the momentum rate and the learning rate are determined using the genetic algorithm. The inputs to the artificial neural networks model are four variables, namely, gross domestic product, industrial structure, total population...
The continuous soaking process (CSP) is an important step in sugar refinery. This paper presented an intelligent optimization control system (IOCS) to implement the modeling, optimization, and control of the CSP with the help of c-means, clustering and genetic. The results of actual runs demonstrate the validity of the system.
Single neural network is difficult in performing accurate predictions for complex model. A hybrid model, which involves a radial basis function network, a multi-layer perceptron network with back-propagation and a control module, is proposed and used for forecasting complex system. The control module serves as a linear mapping network which combines the outputs of two neural networks to gain the final...
This paper presents a simulation of Neuro-Fuzzy application for analysing studentspsila performance based on their CPA and GPA. The analysis is an extension of our previous study, which was called an analysis on studentpsilas performance using fuzzy systems. The main function of this analysis is to support the development of intelligent planning system (INPLANS) using fuzzy systems, neural networks,...
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