Parameters Of RBF Neural Network Research Articles

Exploring the Practice of Cultivating Business English Talents from the Perspective of Artificial Intelligence

Abstract In the context of the development tendency of artificial intelligence, the paper describes the opportunities and challenges artificial intelligence brings to business English talent training, innovates and reforms innovative business negotiation talent training methods, and develops independent innovation according to the future development trend of artificial intelligence. The paper discusses the practical activities of training business English talents using artificial intelligence. It mainly focuses on the indicators of building the entity model of business English talents training through artificial intelligence technical RBF ne ural network, grasping the genetic algorithm from four aspects such as the number of gender chromosomes, integral function formula calculation, genetic algorithm, RBF neural network parameters to optimize the entity model and genetic algorithm, and optimizing the limitation of RBF neural network according to the genetic algorithm. The RBF neural network entity model with the genetic algorithm is used to implement detailed analysis and scientific research exploration of the business English talent training evaluation system. The database deviation index and precision index values under the two entity models were implemented for comparison operation. The experimental results show that under the deviation index, the RBF neural network entity model, according to the genetic algorithm, converges quickly and longer than the RBF entity model. Regarding the precision index, the genetic algorithm RBF neural network has a higher precision than 0.94 for every 60 teams, while the RBF model has a precision of 0.84. This research allows for a more accurate analysis of the strengths and weaknesses of school students in business English. Teachers need to improve their academic performance and quality based on their strengths and weaknesses to facilitate the shaping of more excellent business English talents for China. It is important to explore the way of training business English talents under artificial intelligence to accelerate the innovative development of English education and training in China and the trend of English development.

Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

AbstractIn order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.

W44. OXTR DNA METHYLATION DIFFERENTIATES MEN ON THE OBESITY SPECTRUM WITH AND WITHOUT AN EATING DISORDER

The facts show that multi-instance multi-label (MIML) learning plays a pivotal role in Artificial Intelligence studies. Evidently, the MIML learning introduces a framework in which data is described by a bag of instances associated with a set of labels. In this framework, the modeling of the connection is the challenging problem for MIML. The RBF neural network can explain the complex relations between the instances and labels in the MIMLRBF. The parameters estimation of the RBF network is a difficult task. In this paper, the computational convergence and the modeling accuracy of the RBF network has been improved. The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning. In the current study, a hybrid algorithm was developed to estimate the RBF neural network parameters (the weights, widths and centers of the hidden units) simultaneously. The algorithm uses the robustness of the GBMO to search the parameter space and the efficiency of the gradient. For this purpose, two real-world MIML tasks and a Corel dataset were utilized within a two-step experimental design. In the first step, the GBMO algorithm was used to determine the widths and centers of the network nodes. In the second step, for each molecule with fixed inputs and number of hidden nodes, the parameters were optimized by a structured nonlinear parameter optimization method (SNPOM). The findings demonstrated the superior performance of the hybrid algorithmic method. Additionally, the results for training and testing the dataset revealed that the hybrid method enhances RBF network learning more efficiently in comparison with other conventional RBF approaches. The results obtain better modeling accuracy than some other algorithms.

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection.

Short-term load forecasting based on AP similar days and FISOA-RBF

In order to improve the accuracy of electrical load forecasting for large public buildings, we propose a forecasting model for the short-term load of large public buildings based on affinity propagation (AP) similar days selection and the fusion improvement seeker optimization algorithm-radial basis function (FISOA-RBF) neural network by considering the weather information, date type and other influencing factors. In order to overcome the influence of external environment on the accuracy of building electrical load forecasting, AP algorithm is used to select similar days of short-term electrical load. The structural parameters of RBF neural network are optimized by FISOA which uses the fusion improvement theory to further improve the prediction accuracy and the learning speed of RBF neural network. Finally, the similar daily load data are used to train an optimized FISOA-RBF to predict the short-term electrical load of buildings. In order to validate the effectiveness of the proposed model, the exhaustive experiments are conducted in comparison with RBF, PSO-RBF and SOA-RBF methods. The experimental results indicate that the proposed model outperforms the other models: the mean absolute percentage error (MAPE) is reduced by 93.05%, 83.60% and 71.13%, and the average prediction speed is increased by 54.34%, 39.25% and 23.96%, and thus demonstrate that AP-FISOA-RBF model in prediction accuracy and speed of prediction performance is better than other three RBF-based methods.

Robot learning system based on dynamic movement primitives and neural network

In the process of Human-robot skill transfer, we require the robot to reproduce the trajectory of teacher and expect that the robot can generalize the learned trajectory. For the trajectory after generalization, we expect that the robot arm can accurately track. However, because the model of the robot can not be accurately obtained, some researchers have proposed using a neural network to approximate the unknown term. The parameters of the traditional RBF neural network are usually selected through the empirical and trial-and-error method, which maybe biased and inefficient. In addition, due to the end-effector of the mechanical arm trajectory will be constantly changing according to the needs of the task, when the neural network of compact set cannot contain the whole input vector, the neural network cannot achieve the ideal approximation effect. In this paper, the broad neural network is used to approximate the unknown terms of the robot. This method can reuse the motion controller that has been learned and complete other motions in the robot operating space without relearning its weight parameters. In this paper, the effectiveness of the proposed method is proved by the ultrasound scanning task.

Assessment of vehicle comprehensive maintenance capability based on RBF neural network

To solve the problems of experience-based and subjective-based methods of capability assessment on vehicle maintenance, a RBF neural network model is established, which takes the 7 key factors of vehicle maintenance capability as input, and takes the comprehensive maintenance capability as output. The self-organization learning method and least-squares method are used to determine the weight parameters of RBF neural network, based on which the comprehensive maintenance capability method of vehicle is designed. Simulation results show that the final error of the trained RBF neural network is 0.0073, and the percentage errors of neural network calculated based on test samples are less than 5%, which indicates that the established RBF neural network could assess the comprehensive maintenance capability method of vehicle effectively, which means the neural network is more practical.

Application of RBF neural network optimal segmentation algorithm in credit rating

Credit rating is an important part of bank credit risk management. Since the traditional radial basis function network model is more susceptible to outliers and cannot effectively process the classification data, it is very sensitive in terms of the initial center and class width of the selected model. This paper mainly studies the application of the radial basis function neural network model combined with the optimal segmentation algorithm in the personal loan credit rating model of banks or other financial institutions. The optimal segmentation algorithm is improved and applied to the training of RBF neural network parameters in this paper to increase the center and width of the class, and the center and width of the RBF network model are further improved. Finally, the adaptive selection of the number of hidden nodes is realized by using the differential objective function of the class to adjust dynamically the structure of the radial basis function network model, which is used to establish the credit rating model. The experimental results show that the improved model has higher precision when dealing with non-numeric data, and the robustness of the improved model has been improved.

Research on Optimum Algorithm of Charging Pile Location for New Energy Electric Vehicle

Artificial intelligence algorithms such as ant colony algorithm and neural network do not need to rely on a large amount of gradient information when solving, especially for large-scale complex optimization problems which are difficult to solve by traditional methods, which provides a new perspective and thinking direction for solving such problems. Based on the analysis of the principles and advantages and disadvantages of RBF neural network and ant colony algorithm, this paper proposes a RBF neural network based on genetic mutation improved ant colony clustering algorithm to evaluate the location of charging station. The improved ant colony clustering algorithm based on genetic variation is used to determine the number of hidden layers of RBF neural network, so as to solve the problem that the initial parameters of RBF neural network can not be accurately selected without scientific methods. An example is used to prove the scientific and effectiveness of this method. Finally, the optimal scheme of charging station location is determined by comparing the comprehensive ranking values obtained by various methods used in this paper to judge the advantages and disadvantages of charging station location.

Option Pricing Model Based on Newton-Raphson Iteration and RBF Neural Network Using Implied Volatility

As option is a kind of significant financial derivatives, option pricing will affect both the risk and profit of the investment. This paper proposed an option pricing model based on RBF neural network combined with the Newton-Raphson iteration method which is used to obtain the implied volatility. First, considering implied volatility includes investors’ expectation about the changes of future price options. Newton-Raphson iteration method is used to obtain the implied volatility by rolling estimation which is also added into the RBF neural network model. Then, RBF neural network is trained based on Black-Scholes model. Self-organizing learning and the least square method are used to optimize the parameters of RBF neural network. At last, empirical study and analysis with 10 50ETF stock options chosen from Shanghai Stock Exchange market have been performed, the result shows that the accuracy of the proposed model is better than the traditional BP neural network and B-S model and the effect of option pricing using by implied volatility is also better than others.

Network Intrusion Prediction Model based on RBF Features Classification

According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

The facts show that multi-instance multi-label (MIML) learning plays a pivotal role in Artificial Intelligence studies. Evidently, the MIML learning introduces a framework in which data is described by a bag of instances associated with a set of labels. In this framework, the modeling of the connection is the challenging problem for MIML. The RBF neural network can explain the complex relations between the instances and labels in the MIMLRBF. The parameters estimation of the RBF network is a difficult task. In this paper, the computational convergence and the modeling accuracy of the RBF network has been improved. The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning. In the current study, a hybrid algorithm was developed to estimate the RBF neural network parameters (the weights, widths and centers of the hidden units) simultaneously. The algorithm uses the robustness of the GBMO to search the parameter space and the efficiency of the gradient. For this purpose, two real-world MIML tasks and a Corel dataset were utilized within a two-step experimental design. In the first step, the GBMO algorithm was used to determine the widths and centers of the network nodes. In the second step, for each molecule with fixed inputs and number of hidden nodes, the parameters were optimized by a structured nonlinear parameter optimization method (SNPOM). The findings demonstrated the superior performance of the hybrid algorithmic method. Additionally, the results for training and testing the dataset revealed that the hybrid method enhances RBF network learning more efficiently in comparison with other conventional RBF approaches. The results obtain better modeling accuracy than some other algorithms.

Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

An improved RBF neural network method for information security evaluation

Itis well-known that information security means the protection of information,and ensuring the availability, confidentiality and integrity of information. The purposeof this paper is to present an improved RBF neural network method forinformation evaluation. Ant colony optimization is a multi-agent approach fordifficult combinatorial optimization problems, which has been applied tovarious NP hard problems. Here, ant colony optimization algorithm is applied tooptimize the parameters of RBF neural network. In this paper, we employ “unauthorized access”, “unauthorized accessto a system resource”, “data leakage”, “denial of service”, “unauthorizedmodification data and software”, “system crash” as the features of informationsecurity evaluation. It is indicated that the information security evaluation error of the improvedRBF neural network is smaller than that of the RBF neural network. Thus, theimproved RBF neural network is very suitable for information security evaluation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4806

Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model

Personal credit evaluation is the basic method for the commercial banks to avoid the consumer credit risk. On one hand, the credit behavior of individuals is complex; on the other hand the personal credit assessment system in our country is not sound, assessment methods are mostly objective, therefore, more and better scientific methods for credit risk assessment need to be introduced. This paper proposed a method for personal credit evaluation based on PSO-RBF neural network, which used PSO algorithm to optimize the parameters of RBF neural network, then applied the optimized RBF neural network in the personal credit evaluation. This method combined the global searching ability of PSO algorithm and the high effectiveness of local optimize of RBF together, overcame the unstabitily of PSO algorithm and the drawback of RBF which easily leads to local minimum. The result shows that the personal credit assessment method based on PSO-RBF neural network is highly accurate in classification and prediction, and is suitable in personal credit assessment and prediction.

System Modeling and Application in Gearbox Fault Diagnosis Based on EMD and ARX Model

The EMD method as a means of signal pre-processing is used to decompose the vibration signal of gearbox into a number of IMF components, using the largest energy component as an effective component of the output response signal and the input shaft rotate speed or torque signal as the system input signal, then the time-series ARX model is established, in this paper. Extracted model parameters are as a feature vector of recognized working state of gearbox, and as input parameters of RBF neural network to achieve automatic recognition of faults in the gearbox. The results show that: ARX model can enhance the anti-jamming capability of model, and have great significance for improving the accuracy of fault diagnosis by calculating the fluctuations of input signal into the model.

Study on Dynamic Load Balance Method Based on Genetic Algorithm and RBF Neural Network

Dynamic load balance is the critical pivotal role of network parallel and distributed computing. In order to solve the drawbacks of BP neural network, RBF neural network(RBFNN) is applied to dynamic load balance of network. And genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network, the method is well suited for searching global optimal values. In the paper, genetic algorithm and RBF neural network (GA-RBFNN) is adopted to dynamic load balance of network. The cases are applied to study the ability of dynamic load balance. The experimental results indicate that GA- RBF neural network is better dynamic load balance method than BP neural network.

신경망을 이용한 이동로봇 궤적제어기 성능개선

A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.