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Coordination plays a critical role in basketball training, significantly influencing athletes' performance in actual games. To enhance the overall technical and tactical application level of Liaoning Petrochemical University women's basketball players, this study investigates the evaluation of training coordination. Because traditional backpropagation (BP) neural network evaluation methods exhibit low accuracy, a radial base function (RBF) evaluation prediction model based on cooperative game theory is proposed. An evaluation index system for the training coordination of women's basketball players is established to calculate the weights of each index and rank their importance, and the RBF neural network is utilized for evaluation and prediction. Using actual training data from the university's female basketball players, the study analyzes balance, reaction ability, agility, and processing speed during training. Simulation experiments demonstrate that the proposed method achieves considerable accuracy, validating its effectiveness. This provides a robust technical tool for real-time monitoring of training outcomes.

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

This study first analyzes the features and defects of each product design evaluation method. Then radial basis function (RBF) network is used for the modeling for sowing machines. Considering the characteristics of sowing machines and the general method of design evaluation of electromechanical equipments, 7 primary evaluation indicators for sowing machines are identified, including overall design, form, human-machine interface and color. These primary evaluation indicators were subdivided into 18 secondary indicators. Survey on these indicators was performed by professionals and the scores are assigned to 18 indicators collected from 26 samples. Thus the comprehensive evaluation score of the indicators is calculated using image scale method. The scores of the evaluation indicators are taken as input and the comprehensive evaluation score as the output, then the RBF network for design evaluation is built. After training and verification using 26 samples, it is found that the RBF-based design evaluation model achieves better prediction performance than the BP-based model.

This paper proposed a circular particle swarm optimization least squares (CPSOLS) method which is consisted of the regularized least squares (RLS) method and the adaptive particle swarm optimization (APSO) algorithm. The RLS algorithm optimized the parameters of the RBF network, aiming at the phenomenon of RLS trapping in the local minimum, introduced the penalty factor and used the global optimization ability of the particle swarm optimization algorithm to make it out of the local minimum; simplified the structure of the RBF network and improved the generalization ability of the network. The APSO algorithm weakened the precocious converge phenomena of the particle swarm optimization algorithm, adopted the adaptive selection of the nonlinear dynamic inertia weight which is guided by the control factor of the battery external characteristic temperature parameters, optimized the link weight of the RBF network, improved the state of charge (SOC) estimation accuracy and real-time performance of the RBF network. Using the Arbin multifunctional battery test system BT2000 to collect the sample data of the battery external characteristic parameters, and using the sample data to train and optimize the RBF neural network, and estimate the SOC of the batteries. The results showed that the optimized RBF network improved the SOC estimation accuracy and real-time performance.

오래 전부터 연료의 가격은 상승하고 있다. 제조업체는 보증을 통해 실용적인 대안을 찾고자 전기와 강력한 바이오 연료를 이용하여 차량의 성장가능을 연구하고 있다. 이제, 이러한 녹색 환경(emission) 관련된 보증은 보증기간이 확장되며, 이러한 보증을 '수퍼 보증" 이라 불린다. 본 논문의 주요 결과는 라돈 변환의 역행렬을 보증공간의 수치를 줄이기 위해 사용되며, 응용 프로그램 및 RBF 네트워크를 사용하여 대략적인 이변량의 보증 기능에 새로운 방법을 제시한다. 이 방법은 다음과 같은 단계로 구성되어 있다. 첫째, 라돈 변환을 이용하여, 이변량 보증 함수의 1 차원 함수를 줄일 수 있다. 둘째, 1 차원 함수의 각 신경 서브 네트워크와 신경 네트워크 기법을 사용하여 근사할 수 있다. 셋째, 이러한 신경 sub-networks 형태로 최종 근사 신경망 함께 결합 된다. 넷째, 라 돈 변환의 역함수 값을 사용 하여 최종 근사 신경 네트워크에 우리가 주어진 함수 근사화를 얻을 수 있다. 또한, 우리는 자동차 회사의 일부 그린 보증 데이터를 가지고 위의 방법을 적용한다.

As the price of traditional fuels soar, the alternatives are becoming more viable. And manufacturers are promoting the growing viability of electric and biofuel-powered vehicles through longer warranties. Now, these longer green environment (emission)warranties, sometimes called extended warranties or “super warranties,” have been adapted. The main result of this paper is to present a new method to approximate a bivariate warranty function by using Radial Basis Function Network with application of Radon Transform and its inverse which is used to reduce the dimension of the warranty space. This method consist of the following stages: First, by using the Radon Transform, the bivariate warranty function can be reduced to one dimensional function. Second, each of the one dimensional functions is approximated by using neural network technique into neural sub-networks. Third, these neural sub-networks are combined together to form the final approximation neural network. Four, by using the inverse of radon transform to this final approximation neural network we get the approximation to the given function. Also, we apply the above method to some green warranty data of automotive vehicle company.

Biometric has been implemented on numerous public facilities to enhance the security system. Fingerprint and face are the most popular biometric. Emerging technology has introduced potential biometric such as palm print, lips, teeth, vein and ear. However, most of this biometrics requires a special device to capture it. Thus, the implementation of such system will be costly. Iannarelli [1, 2] has proved that ear biometric is having a great potential for identifying a person. In this research work, an attempt is made to improve the detection and finally to segment the human ear from the whole image of human’s head. The success of this stage is very important for achieving the later goal, such as recognition and classification. This paper introduces a novel method for ear segmentation. Proposed method is based on morphological analysis fused with RBF neural network. Experiment shows that the proposed method has delivered a promising result.

Aiming at the complexity of ground objects in urban area, and the difficulty in distinguishing ground objects using spectral characteristics, we extracted normalized different indexes, namely Modified Normalized Difference Water Index (MNDWI), Soil Adjusted Vegetation Index (SAVI ) and Normalized Difference Building Index (NDBI) , as the key auxiliary information for land use classification of urban area. To solve problems of RBF neural network, such as local minimum values and discrete output value in output layer, we used max-min distance means to initialize RBF center, and introduced equilibrium factor into Gauss function to improve RBF neural network learning algorithm. On this basis, a new urban area classification model was proposed based on improved RBF network and normalized difference indexes. At last, NanChong city in SiChuan province of China was taken as the study area, and TM images was used as experiment data to test the model proposed in this paper. The results showed that, based on the improved RBF network, with the help of spectral band information, the classification overall accuracy was 89.97%, Kappa coefficient was 0.88; using both spectral band information and normalized difference indexes, the classification overall accuracy was 95.02%, Kappa coefficient is 0.94, the classification overall accuracy was improved by 5.05%. Also, the experiment results showed that, with the help of spectral band information and normalized difference indexes, the classification overall accuracy of MLC, BP and improved RBF network was 90.12%, 93.63%, 95.02%, respectively, which means RBF has an advantage of fusing geological parameters in classification.

Artificial fish-swarm algorithm is a realization model of the swarm intelligence optimization algorithm. It uses the optimization model of imitated nature fish for feeding from top to bottom, clusters and rear, local optimization by individual fish, achieve the purpose of global optimal values highlighted in the groups. RBFNN based on the AFSA can accurately find the optimal solution quickly and ensure the diversity of artificial fish. It is easier to find the global optimal point of optimal fish. This design uses second-order pendulum as a controlled object, using artificial fish swarm algorithm applied to the neural network training algorithms, building design of RBF Neural networks control module , verifing by Matlab simulation of actual control controller performance.

Wood defect is due to the physiological process, genetic factor or affected by the external environment in the growth period. These defects will reduce the utilization value of wood. However, it is very difficult to determine whether there are defects exist, and the degree of defects. Therefore, the effective detection of wood defect information is particularly important. A new wood defect detection method by using RBF neural network was proposed in this paper. The new RBF defect detection method can be divided into the following main steps: (1) Detect wood defects by using X-ray nondestructive testing technology. (2) Deal with defect images by using digital image processing technology. (3) Analyze the information of different defects, and extract the characteristic value of wood defects. (4) Then, the RBF neural network model was constructed. (5) Finally, the RBF neural network is trained with the known samples and simulated with the unknown samples. The experimental results shown that the RBF neural network method was effectively detect the two typical wood defects. This method provides an important theoretical basis to realize the wood defect automatic detection.

In the networked control system, the control interference, measurement noise and time delay make traditional digital PID algorithm not reach stable state. On the basis of simple digital PID algorithm, Kalman filter is first introduced, the effect of the interference and noise is decreased, and stability is improved. Then RBF (Radial Basis Function) neural network is used, Jacobian array is computed, the three parameters of PID algorithm are adjusted. Furthermore, the resistance integral saturation is used to limit the size of control quantity. Finally the simulation research on a DC (Direct Current) motor is done, and the simulation results show the effectiveness of the proposed algorithm when time delay, noise and interference are all large.

Along with the development of shipping business, ships are becoming bigger, faster and more intelligent. Thus better performance of maneuver is demanded. To research for better control strategies, it is necessary to adopt new control theories and techniques. The application of neural network techniques and backstepping algorithm in ship motion control became an important research area in recent years. Aiming at the nonlinear of ship motion, also for application of control strategy, control strategy based on the RBF neural network and backstepping algorithm is proposed. The strategy employs the RBF neural network to approximate and substitute the system, and employs adaptive law designed by backstepping algorithm to adjust the weight of the RBF neural network. Finally, the proposed strategy was applied in ship course tracking control simulation and the satisfying performances demonstrate the feasibility and effectiveness of the ship control strategy.

Brushless DC motor (BLDCM) has been widely used in industries because of its advantages. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. It is difficult to get satisfied control performance with conventional PID controller. By using self-learning and adaptive ability of neural network to unknown information, an intelligent PID control method based on adaptive radial basis function (RBF) neural network is proposed. Connection weight of neural network is updated according to the motor speed and phase current. The duty ratio of pulse width modulation (PWM) is adjusted to regulate the speed of BLDCM. The effectiveness of the proposed control method was validated with simulation and experiment. Simulation and experiment results showed that compared with the conventional PID controller, the proposed controller has less overshoot, faster response speed, stronger ability of anti-disturbance.

In this paper, feature extraction and facial recognition are studied in order to resolve problems like high-dimension problem, small size samples and no-linear separable problem that exist in facial recognition technology. In the part of feature extraction we use a Discrete Cosine Transform (DCT) algorithm, to extract the input features in building a face recognition system. The RBF neural network, which represents brilliant performance on small training sets, non-linear separable and high-dimension pattern recognition problems in the recognition stage, is used for pattern classification. The proposed approach is validated with the ORL database. Experimental results demonstrate the effectiveness of this method in the performance of face recognition.

A new channel estimation method of neural network based on complex radial basis function (CRBF) is proposed to enhance the anti-interference ability of traditional pilot frequency estimation algorithm in power line communication (PLC). This method builds up a new channel model of complex field signals in PLC. The complete response model was established by using transmitting terminal’s pilot signal as input sample data, pilot signal’s frequency response as output sample data, and pre-setting mean square error (MSE) and diffusion constant. Computer simulations show that compared with the traditional algorithm the channel estimation was more accurate and had lower MSE and bit error rate (BER).

The acoustic emission (AE) technology can be used to assess the security condition of oil storage tank without opening pot. Signal recognition is a foundation to analyze the corrosion status for oil storage tanks. Because of inadequateness of the analysis method of parameters, a new acoustic emission signal recognition method is proposed based on wavelet transform and RBF neural network. AE signal was decomposed to 6 layers by db2 wavelet and the space energy of 6-layer detail features is regarded as the vector of the AE signal characteristics. RBF neural network is designed by considering the characteristics of AE signal. The RBF neural network is trained by using the pattern known of acoustic emission signal. RBF network is used to classify experiments to corrosion, crack and condensation acoustic emission signal. The experimental results show that the recognition rate of RBF neural network reaches 93.3%, which reveals the advantage of the acoustic emission signal of neural network recognition. It has some significance of the quantitative analysis to the safety situation of oil storage tanks.

Aesthetic factors are an essential part of farm machinery development and design. In this paper, we take seeding machinery, typical farm machinery, as an instance and establish an aesthetic evaluation model for seeding machinery based on RBF neural network to predict design effects, which will provide important evidence to intelligent design of seeding machinery. Furthermore, aesthetic characteristic elements of seeding machinery are analyzed to establish an evaluation index system that is classified into three levels, of which the first-level index include technical and formal beauty, the second-level index contains beauty of function, material, shape and color and the third-level index comprises 17 factors. RBF neural network is employed to establish a mathematical model, where input layer is composed of 17 low-level evaluation index values and output layer is the comprehensive evaluation values of aesthetics by experts. Training and verification of 22 samples found that predictive effects of RBF neural network-based model on the evaluation model of seeding machinery modeling are superior to BP network-based prediction model, for it can better deal with uncertainties.

Due to the use of time, machine wear degree, coal and other reasons, the original set parameters of the boiler have been unable to meet the control requirements, therefore using a large amount of data to build a real model of the power station based on the neural network, therefore, to establish a boiler combustion optimization neural network model by using of the power plant operating data. According to the shortcomings on RBF neural networks traditional training methods with slow convergence speed, easy to fall into the local minimum. Firstly, this paper Set the model to single input and single output system as the research object, optimize neural network by the particle swarm optimization algorithm. Finally, this modeling method is expanded to the multiple input multiple output system field. Use MATLAB to establish the simulation model and the simulation research, the simulation results show that improved method for combustion boiler system efficiency has been significantly improved, combustion efficiency of the entire system reached 94%, the accuracy of the system model was significantly better than ordinary neural network, system training error controls in less than 5% .We can see that the improved method is feasible and effective.

To effectively predict auto sales and improve the competitiveness of automotive enterprise, the characteristics of actual auto sales were analyzed, owing to the seasonal fluctuations and the nonlinearity of monthly sales, the combination forecasting model based on seasonal Index and RBF neural network was proposed. The weights of the two single models were computed using mean absolute percentage error and the sum of square error respectively, the result shows that mean absolute percentage error is more effective. Finally, the prediction accuracy of different models was compared based on the criteria of MAPE and RMSE, and the effectiveness of the method was proved, the proposed model can take advantage of the strengths of the two single models, the results indicate that the combination forecasting model suitable for auto sales has high prediction accuracy, which can provide a certain reference to auto sales forecasting.

Aiming at the distributed containment control problem for multi-robot system with dynamic leaders, we use binocular vision as the sensing device, so firstly, a binocular visual model for dynamic target is established by introducing the pseudo depth variable, and it is used to provide real-time status information for the followers. Then, in the case that each follower can only get information from part of the leaders, even individual follower can not get any information from the leaders directly, the topological relationship among robots is built by using the graph theory. Once more, RBF neural networks are employed to approximate the uncertainties in the system model of followers, and L2 robust control laws are used to restrain the influences of the approximation errors. Finally, a simulation is carried out on multiple 2 degrees-of-freedom (DOF) robots, and results validate the designed controllers can drive the followers converge to the convex hull spanned by the leaders asymptotically and realize synchronization motion ultimately.