Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network

2007 ◽  
Vol 364-366 ◽  
pp. 25-29
Author(s):  
Fei Hu Zhang ◽  
D.J. Chen ◽  
L.J. Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Global Optimum ◽  
Numerical Control ◽  
Local Optimum ◽  
Interpolation Algorithm ◽  
Aspheric Surfaces ◽  
The Neural Network ◽  
Circular Curves ◽  
Circular Curve

When the Neural Network model is used to interpolate the non-circular curves, there are shortcomings of converging slowly and getting into the local optimum easily. A novel numerical control interpolation algorithm based on the GA (Genetic Algorithms) and NN (Neural Network) was introduced for the ultra-precision machining of aspheric surfaces. The algorithm integrated the global searching of GA with the parallel processing of NN, enhanceed the convergence speed and found the global optimum. At the end, the quintic non-circular curve was taken as an example to do the emulation and experiment. The results prove that this algorithm can fit the non-circular curve accurately, improve the precision of numerical control interpolation and reduce the number of calculating and interpolation cycles.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

A New Optimization Strategy for Chemical Mechanical Polishing Process

2000 ◽  
Author(s):  
Gou-Jen Wang ◽  
Jau-Liang Chen ◽  
Ju-Yi Hwang
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Signal To Noise Ratio ◽  
Global Optimum ◽  
Optimum Process ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Two Stage ◽  
Chemical Mechanical Polishing Process ◽  
Trained Neural Network

Abstract In this paper, a systematic approach to achieve global optimum CMP process is carried out. In this new approach, orthogonal array technique adopted from the Taguchi method is used for efficient experiment design. The neural network (NN) technique is then applied to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. Successively, the global optimum parameters are acquired in terms of the trained neural network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy can perform better then the original approach even though the polishing time is reduced by 1/6.

Genetic Range Genetic Algorithms to Obtain Quasi-Optimum Solutions

2003 ◽  
Author(s):  
Masao Arakawa ◽  
Tomoyuki Miyashita ◽  
Hiroshi Ishikawa
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
New Product ◽  
Global Optimum ◽  
Test Problems ◽  
Local Optimum ◽  
Optimum Solution ◽  
Product Response ◽  
Peaked Function ◽  
Global Optimum Solution

In some cases of developing a new product, response surface of an objective function is not always single peaked function, and it is often multi-peaked function. In that case, designers would like to have not oniy global optimum solution but also as many local optimum solutions and/or quasi-optimum solutions as possible, so that he or she can select one out of them considering the other conditions that are not taken into account priori to optimization. Although this information is quite useful, it is not that easy to obtain with a single trial of optimization. In this study, we will propose a screening of fitness function in genetic algorithms (GA). Which change fitness function during searching. Therefore, GA needs to have higher flexibility in searching. Genetic Range Genetic Algorithms include a number of searching range in a single generation. Just like there are a number of species in wild life. Therefore, it can arrange to have both global searching range and also local searching range with different fitness function. In this paper, we demonstrate the effectiveness of the proposed method through a simple benchmark test problems.

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

scholarly journals Genetic algorithms - variable size populations of chromosomes. An adaptive approach

2018 ◽  
Vol XIX (1) ◽  
pp. 393-399
Author(s):  
Maniu R
Keyword(s):  
Genetic Algorithms ◽  
Negative Impact ◽  
Large Population ◽  
Search Space ◽  
Global Optimum ◽  
Small Population ◽  
Local Optimum ◽  
Variable Size ◽  
Algorithm Operation

The size of the chromosome population is an essential parameter of genetic algorithms. A large population involves a large amount of calculations but provides a complete scroll of the search space and the increased probability of generating a global optimum. A small population size, through the small number of operations required, causes a quick run of the algorithm, with increasing the probability of detecting a local optimum to the detriment of the global one. This paper proposes the use of an adaptive, variable size of chromosome population. We will demonstrate that this approach leads to an acceleration of the algorithm operation, without having a negative impact on the quality of provided solutions.

scholarly journals Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1367
Author(s):  
Xiangyu Han ◽  
Dingkang Li ◽  
Lizong Huang ◽  
Hanqing Huang ◽  
Jin Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Local Optimum ◽  
Line Current ◽  
The Neural Network ◽  
Artificial Neural ◽  
Simple Neural Network

The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

scholarly journals Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

2021 ◽  
Vol 11 (12) ◽  
pp. 5470
Author(s):  
Yulia Shichkina ◽  
Yulia Irishina ◽  
Elizaveta Stanevich ◽  
Armando de Jesus Plasencia Salgueiro
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Genetic Algorithms ◽  
Monitoring System ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Using Data

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.

Study on Displacement Prediction Model of Foundation Pit

2013 ◽  
Vol 834-836 ◽  
pp. 679-682
Author(s):  
Qiang Song ◽  
Jun Jian Zhang ◽  
Yun Sheng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Prediction Model ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Foundation Pit ◽  
Displacement Prediction ◽  
Fitness Evaluation ◽  
The Neural Network

The prediction model is proposed in this paper to predict the displacement of foundation pit. In the model, genetic algorithms is applied to optimize the node function of the neural network (15 node function coefficients are optimized simultaneously). Next, do the further optimization to the model, and GA-transFcn3 Model is established whose fitness evaluation takes into account the multi-step prediction error. Finally, it is verified that the GA-transFcn3 Model created in this article has the desirable prediction accuracy through engineering examples. The establishment of GA-transFcn3 Model can provide researchers and engineers with ideas and methods for the displacement prediction of foundation pit, and can be popularized and applied in practical projects.

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