An Efficient Optimization Algorithm for Extreme Value of Nonlinear Function Based on the SAGA and BP Algorithm

This paper aims to improve the quality of sports development by using particle swarm optimization (PSO) based long and short-term memory network (LSTM) and support vector machine (SVM) based sports performance prediction model (SPPM) to realize the shift to information technology (IT)-based sports governance in the context of the “Double Reduction”. The effects of LSTM and PSO-LSTM are evaluated by testing individual exercise physiological data like heart rate and blood oxygen values. Test the effectiveness of particle swarm optimization neural network’s sports performance prediction model with sample data and conduct comparative experiments with multiple linear regression, genetic algorithm optimization BP neural network, and firefly optimization BP neural network, respectively. Analyzing the characteristics of the extracted samples and performing a paired-sample t-test on the sample size revealed the gap between service perceptions and expectations. The analysis shows that the overall expectation mean of parents is 4.482, and the actual perceived mean is 3.966. All the indexes fail to reach the ideal level of parents, which indicates that the delayed sports service proposed by the institutional sports needs to be further adjusted.

In view of the complex structure of the fire control system, difficulty in obtaining fault information, and multiple fault characteristics. This paper proposes a fire control system fault diagnosis method based on genetic algorithm optimized BP neural network. For the problem of poor prediction accuracy of BP neural network in the operation process, and easy to fall into local extreme value. In order to obtain a better diagnosis effect, the principle of complementary advantages is used to combine genetic algorithm with BP neural network algorithm. The genetic algorithm is used to calculate the initial values of network parameters, optimize the initial weights and thresholds, and find the optimal number of hidden layer nodes. Combined with case analysis, the accuracy of this method is improved compared with traditional BP neural network in solving the problem of fire control system fault diagnosis. At the same time, it proves the effectiveness of the proposed method in fire control system fault diagnosis.

飞机驾驶舱系统自动化水平提高的关键在于科学合理地进行人机功能分配。为了提高飞机驾驶舱人机功能分配的可靠性，本文通过对遗传算法和BP神经网络这两种智能算法进行分析研究，提出了基于遗传BP神经网络的飞机驾驶舱人机功能分配方法。将机组成员飞行时的生理指标HRV2和TLI2作为网络的输入变量，使用遗传算法优化BP神经网络权值和阀值，输出飞行任务自动化等级，进而得到相应的人机功能分配方案。仿真结果表明，相比于传统的BP神经网络，该方法确定的人机功能分配方案可靠性更高。 The key to improve the automation level of the aircraft cockpit system is getting on man-machine function allocation scientifically and reasonably. In order to improve the reliability of man-machine function allocation for cockpit, based on the study of genetic algorithm and BP neural network, we propose a method of using genetic BP neural network in this paper. The input variables of the network are HRV2 and TLI2 which are the physiological indexes of the crew. Geneticalgorithm was used to optimize the weights and bias of BP neural network. The output variable of the network is the levels of automation. In this way, we can get corresponding man-machine function allocation scheme. Compared with the traditional BP neural network, the simulation result shows that this method is more reliable for the man-machine function allocation.

The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization and BP training algorithm to accurately optimize the neural network weights and training the neural network to learn letter recognition algorithm. Add-noise alphabet of MATLAB simulation results show that the new network error recognition rate reduced by 10% compared to BP neural network and the recognition speed is also faster than the traditional BP neural network with accuracy and fast convergence.

In this paper, a hybrid model (BP-GA) including a back propagation network and a genetic algorithm is used to estimate the thermal conductivity of the nanofluids. The genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so that the optimized BP neural network can better predict the function output. In this study, CuO-ZnO hybrid nanofluids with the mass fraction of 0%, 1%, 2%, and 3% were studied at temperatures of 25, 30, 35, 40, 45, 50, 55, and 60 °C, respectively. The thermal conductivity of the hybrid nanofluids base liquid is 20:80, 40:60, 50:50, 60:40, 80:20, and the mass ratio of CuO-ZnO is 50:50. The mass fraction of CuO-ZnO, the temperature and the mixing ratio of different base liquids were used as input parameters, and the thermal conductivity was the output parameter, forming a 3-input ANN neural network. The predicted thermal conductivity results of BP neural network and genetic algorithm optimized BP neural network (GA-BP) were compared with experimental data. The results illustrate that the BP neural network optimized by genetic algorithm exerts a positive influence on the accuracy and stability of the predicted output.

In order to scientifically and accurately evaluate power information system, the new power information risk evaluation method based on the genetic algorithm and BP neural network is presented. The method combining the genetic algorithm and BP algorithm can be used to train the feedforward neural network , namely, first , to use the genetic algorithm to do the global training, then ,to use BP algorithm to do local precise training ,which not only overcomes the drawbacks of the traditional BP network (the training time is long, and the network is easy to fall to local extremum),but also improves the global convergence efficiency. The method was adopted to evaluate the power information system. And findings identify that the new method has distinctive convergence speed and high predicition accuracy, which provides a new concept for power information system risk assessment.

In order to improve the accuracy and speed of image recognition, an improved image recognition algorithm is designed based on genetic algorithm and BP neural network as results of the traditional BP neural network has problems such as structural parameter uncertainty, low convergence rate and local minimum value. Firstly, the color and texture feature of the image are extracted. BP neural network structure parameters are optimized by using BP neural network based on the genetic algorithm. The image recognition process is presented and the image recognition result is integrated according to the evidence theory to obtain the complete image information. The simulation results show that the algorithm has high recognition rate and convergence speed. Under the condition of a small number of training samples, BP neural network still has better generalization ability.

In order to recognize the pattern of the powered type lower limb prosthesis movement accurately, a genetic algorithm and neural network algorithm was proposed in this paper. Gyroscope sensor which was installed in the prosthetic socket and pressure sensors which were installed in the Prosthetic foot were used for data acquisition in the experiment. The mean of the angle was calculated in different motion mode corresponding support phase and swing phase. They were input variables of the BP neural network model. Genetic algorithm was used to optimize the weights and bias of BP neural network. Optimized BP neural network was applied to establish recognition model. The results showed that the proposed method offered the advantages of high precision and fast convergence in contrast with BP neural network.KeywordsProsthesisGyroscopeGenetic algorithmBP pattern recognition

By using Genetic BP neutral network, After evaluating the performance of the six logistics companies using the BP neural network, the genetic BP neural network algorithm, compared with traditional BP algorithm, has made a more accurate prediction about testing samples.

Real estate has been an important part of the Chinese economy. It is of great significance to study the factors influencing commercial, residential housing prices for stabilizing economic growth. In this paper, we designed a new index set according to the principle of economics. On this basis, we proposed a Back Propagation neural network model based on genetic algorithm optimization. The experimental results show that the improved BP neural network based on Genetic Algorithm has a better fitting effect than the traditional fitting model and BP neural network model, and MSE decreases by 35.6% and 14.5%, respectively. Through the improved model, we can reference the study of housing prices, real estate buyers, and investors' decision-making.

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

The sparrow search algorithm (SSA) has received widespread attention as an emerging group intelligence algorithm. In this study, a QSAR (quantitative structure-activity relationship) prediction model for anti-ERa (Estrogen receptor alpha, an important target for the treatment of breast cancer) compounds and their bioactivity data was constructed based on a sparrow search algorithm optimized BP neural network combined with the selected relevant parameter indicators. Then the created model was used to predict the biological activity of the compounds. The results show that the created model not only has a certain self-learning function, but also improves the convergence speed and the accuracy of the prediction results compared with the BP neural network model optimized by genetic algorithm (which is more complex in coding and slower in solving, but has good solution accuracy). Further, it was demonstrated that the use of SSA-BP could improve the prediction of QSAR for anti-ERa compounds.

In the face of more and more faults in coal mine equipment, this paper presents the method of combining genetic algorithm (GA) and BP neural network to predict the failure. According to genetic algorithm has a very slow convergence speed, easy to fall into local optimum, this paper uses chaos and reverse individual learning initialization, followed by the use of differential algorithm to operate on the optimal individual. Finally, the improved fitness function is applied to the selection operation, and the accuracy of operation is improved by mutation probability and crossover probability. The improved algorithm is applied to the BP neural network to improve the training effect. The simulation results show that the proposed algorithm improves the accuracy and stability compared to the traditional BP neural network.

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

The detection of regulatory sequences in DNA is a challenging problem, especially when considered in the context of whole genomes. The degree of sequence conservation of regulatory protein binding sites is often weak, and the sites are obscured by surrounding intergenic sequence. Since structural interactions are vital for protein-DNA interactions, structural representations of regulatory sites can yield a more accurate model and a better understanding of within-site variability. However, the use of multiple alternative representations of DNA introduces a requirement for novel algorithms that can create and test different combinations of DNA features. The Genetic Algorithm Neural Network (GANN) was designed to identify combinations of patterns that can be used to distinguish between different classes of training sequence. GANN trains a set of artificial neural networks to classify sets of sequence using either backpropagation or a genetic algorithm, and uses an ‘outer genetic algorithm’ to choose the best inputs from a pool of DNA features that can include sequence, structure, and weight matrix representations. When trained with a subset of upstream sequences from a whole genome, GANN was able to detect patterns such as the Shine-Dalgarno sequence in Escherichia coli K12, and sequences consistent with archaeal promoters in the archaeon Sulfolobus solfataricus P2. The Motif Genetic Algorithm (MGA) constructs motif representations by concatenating minimal units of DNA sequence and structure. This algorithm was used to model conserved patterns in DNA, including the binding sites for E. coli cyclic AMP activated protein (CAP), integration host factor (IHF), and two different promoter types recognized by alternative bacterial sigma factors. The CAP models were used to detect other putative binding sites in upstream regions of the E. coli K12 genome, while attempts to train an accurate model of IHF binding sites revealed an important role for structural representations in motif modeling.