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Aiming at the problem that the dual-frequency ultrasonic extraction of puerarin is difficult to detect effectively and efficiently with the method of manually watching and off-line detection, a method of soft sensor modeling is proposed. In the method, the genetic algorithm and the support vector machine (GA-SVM) are combined to build the soft measurement model of the puerarin extraction. By using...
Road transportation is one of the main source of carbon dioxide emissions, and it is imperative to estimate the carbon dioxide contribution of road transportation precisely, so that carbon dioxide emission-reduction measures can be designed and implemented appropriately. Microscopic emission models and GPS trajectories are widely used in estimating carbon dioxide emissions. Microscopic emission models...
In order to realize the rapid satellite selection of BDS / GPS dual-mode navigation system under the premise of satisfying the normal positioning accuracy, a new fast satellite selection algorithm is proposed. The algorithm combines the traditional satellite selection algorithm with genetic algorithm. The satellite area is divided by the elevation angles information of the visible satellite, according...
The paper introduces the principle of traditional PID algorithm, analyzes its advantages and disadvantages, and proposes a new control scheme — variable structure fuzzy neural network for such a nonlinear and complex system of automatic Gauge control (AGC). Variable structure fuzzy neural network combines the advantages of neural network and fuzzy control, and also adds a genetic algorithm to optimize...
This paper first establishes the kinematic model and dynamic model of Selective Compliance Assembly Robot Arm(SCARA) robot based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation, the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish...
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
In the context of the educational quality evaluation measured through standardized tests, this article aims to select the context variables that have a greater contribution in the differentiation of the categories of the 2015 SIMCE math score, for eighth grade students of the region of La Araucanía, Chile. Based on a cross-sectional research, a supervised classification design was implemented, defining...
This article presents a way to solve Resource-Constrained Project Scheduling Problem with genetic algorithm and demonstrates the high relevance of metaheuristics for solution this type problem. Also shows various benchmarks with others heuristics. The benchmark performed compares directly to a relatively new heuristic and to several other works performed. The genetic algorithm together with the approach...
This work presents the results of a swing-up controller applied to a Quanser rotary inverted pendulum simulated in Matlab. The objectives were to invert the pendulum in minimum time and to obtain a final position and velocity that could be stabilized in the inverted position. The controller's parameters were tuned using a genetic algorithm specially designed for this task. During the simulation tests,...
This work addresses directivity improvement of a fan-beam antenna using local constrained optimization. More specifically, it demonstrates that adjustment of elementary patch sizes of a gridded patch antenna considerably improves antenna directivity without increasing its footprint. This indicates that utilization of all available degrees of freedom, i.e., genetic algorithm (GA) optimization and continuous...
A approach using the adaptive genetic algorithm (AGA) is proposed for array failure correction in digital beam forming of arbitrary arrays. In this article we use adaptive genetic algorithm (AGA) change the excitation of elements to improve the performance of array with failed elements. Numerical examples are presented to show the effectiveness of the approach.
As the advantages of good convergence and less computing time in estimating multiple parameters, genetic algorithm (GA) has been applied to estimate the parameters of the multiple scattering centers. It is found that various objective functions (OF) selected in GA yields different estimation results. The performance comparison among different OFs are investigated in detail, based on which, an optimal...
In this paper, a novel method for the optimal design of sparse concentric ring arrays (CRA) is proposed. Owing to the property of Bessel function, the synthesis of CRA can be recast as a sparse constrained approximation problem and solved with compressive sensing method. A representative numerical experiment is provided to assess the effectiveness and advantages of the proposed method in comparison...
Evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy...
Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance. This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization...
The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker...
This paper presents multiple variances of selection operator used in Non-dominated Sorting Genetic Algorithm II applied to solving Bi-Objective Multi-Skill Resource Constrained Project Scheduling Problem. A hybrid Differential Evolution with Greedy Algorithm has been proven to work very well on the researched problem and so it is used to probe the multi-objective solution space. It is then determined...
This paper presents a deep analysis of literature on the problems of optimization of parameters and structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there is suggested a new algorithm for neural network structure optimization, which is free of the major shortcomings of other algorithms. The paper describes a detailed...
The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of...
Traditional plate-fin heatsinks are used in abundance in data centres and telecommunication systems for electronic integrated circuit and component cooling, without much regard for geometric shape optimisation. Any improvements in the effectiveness of the heatsinks impacts the energy consumed by the information communication and technology (ICT) centres and promote a more sustainable use of raw materials...
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