Over the past few decades, the field of environmental atmospheric pollution prediction has undergone relentless exploration and progress, with significant advancements in predictive methodologies and technical measure...
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Over the past few decades, the field of environmental atmospheric pollution prediction has undergone relentless exploration and progress, with significant advancements in predictive methodologies and technical measures. However, further improving the accuracy of air pollution predictions and flexibly predicting them remains at the core of this field. To address the issues of prediction accuracy and flexible forecasting for air pollutants, this paper proposes a nitrogen compound concentration prediction model based on a Genetic Algorithm and Particle Swarm Optimization optimized Back Propagation (BP) neural network, deployed on a quadruped robot platform. Under the framework of the BP neural network, this model integrates the Genetic Algorithm and Particle Swarm Optimization to form a hybrid optimization method. This method can optimize the initial weights of the BP neural network, thereby significantly enhancing the generalization performance of the neural network and preventing the network from prematurely converging to local optimal solutions. The model is used to predict the concentration of NO2, and it is also compared with other models through experiments. The results of the prediction and comparative experiments show that this prediction model demonstrates high prediction accuracy. Its prediction results outperform other models, achieving the expected effect.
The purpose of the present article is to introduce theoretical and algorithmic approaches to address the problem of finding optimal test-control incomplete block designs with unequal block sizes where intra-block obse...
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The purpose of the present article is to introduce theoretical and algorithmic approaches to address the problem of finding optimal test-control incomplete block designs with unequal block sizes where intra-block observations are correlated. Theoretical approach is used to find E-tc-optimal designs analytically. In addition, due to the computational complexity of theoretical methods, in this article a two-phase optimization algorithm is proposed to construct phi-optimal or nearly phi-optimal designs. The effectiveness of the proposed algorithm is validated by comparing our results with optimal designs presented in several prior studies. Our algorithm has the advantages of being independent from the sizes of blocks, structure of correlation, and the optimality criteria. Moreover, it takes only a few minutes to obtain the optimal designs.
The work presented in this paper focuses on Quadrirotor UAV navigation control. Adynamic model of the Quadrirotor UAV is described followed by a control approach design based on conventional PID and PID optimized by G...
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ISBN:
(纸本)9781728112923
The work presented in this paper focuses on Quadrirotor UAV navigation control. Adynamic model of the Quadrirotor UAV is described followed by a control approach design based on conventional PID and PID optimized by Genetic Algorithm (ga) technique applied to the vertical position (z) control. Controller parameters optimization is based on a fitness function time weight square error (ITA). To approve our approach we have used different values of ga parameters in different simulations in matlab-simulink environment. The simulation obtained results illustrate the efficiency of our control design and open the perspectives for future works.
This paper put forward the realization of the self-automation role, which has leaning ability and dynamical acclimatization. First of all, BP algorithm of artificial neural net(ANN) is improved, the self-adjusted algo...
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ISBN:
(纸本)9780769538655
This paper put forward the realization of the self-automation role, which has leaning ability and dynamical acclimatization. First of all, BP algorithm of artificial neural net(ANN) is improved, the self-adjusted algorithm of all parameters has been proposed for the back-propagation learning, which can make the selection of hidden layer units and rate of studying easily in the course of training, reduce artificial influence and improve the adaptive ability of rate of studying and neural net. Secondly, Genetic algorithms (ga) has been optimized from primitive colony, selective manipulation, intercross manipulation. At the same time, methodlogy of ANN was integrated with ga and self-learning models of NPC were created to control their behaviors. At last, the experimental results have shown that self-learning system of NPC provides artificial behaviors with more automation and intelligence.
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