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Sports rehabilitation is a complex process involving a variety of physiological and psychological factors, which are often difficult to accurately capture with traditional quantitative methods. An analysis method of key indexes in sports rehabilitation is based on fuzzy neural network. A fuzzy neural network, as an intelligent computing model combining fuzzy logic and neural network, can deal with uncertainty and fuzziness effectively and has a unique advantage in the analysis of key indicators in sports rehabilitation. The basic principle and structure of fuzzy neural network include how to process fuzzy information and pattern recognition. Then, this paper describes in detail how to apply fuzzy neural networks to the analysis of the key indicators of sports rehabilitation, including the steps of data preprocessing, network training and result evaluation. Through the analysis of athletes’ physiological data, training load, recovery state and other key indicators, fuzzy neural networks can provide personalized guidance and suggestions for rehabilitation training. Robustness is required when dealing with complex nonlinear problems, and overfitting problems may occur when the amount of data is insufficient. The validity and accuracy of fuzzy neural networks in the analysis of key indicators of sports rehabilitation were verified.

The detection method of microblog false information has been constructed based on block matching and fuzzy neural network to improve the detection accuracy of microblog false information effectively. With this method, we can calculate the rank distance and sample entropy of microblog data according to the evaluation word rank vector of microblog false information, carry out the block matching of false information in fuzzy data set and input the characteristic quantity of microblog false information extracted into the fuzzy neural network classifier for data classification and recognition. So that it has achieved the optimized detection of false information and improved the judgment ability of false information. Finally, the key factors that affect the algorithm are deeply studied through simulation experiments according to the real data of Sina microblog, and the performance state between the proposed algorithm and Fuzzy C-means and Spectral Analysis algorithms is compared and analyzed correspondingly. The results show that the algorithm has good adaptability.

This paper presents an evolvable version of a novel subsethood product fuzzy neural inference system (ESuPFuNIS). The original SuPFuNIS model²⁰ employs only fuzzy weights, and accepts both numeric and linguistic inputs. All numeric inputs are fuzzified using a feature specific fuzzifier. The model composes fuzzy signals from the input layer with fuzzy weights using a mutual subsethood measure. Rule nodes use a product aggregation operator. Outputs from the network are generated using volume defuzzification. Here we replace the original gradient descent learning procedure with a genetic optimization technique and report considerable improvements in classification accuracy and rule economy on three benchmark problems. Real-coded genetic algorithms (RGA's) have been employed to search for an optimal set of network parameters. We demonstrate the classification capabilities of the network on Ripley's synthetic two class data, Iris data and Forensic glass data. In all the problems considered, the GA based classifier performs better than its gradient descent counterpart in terms of classification accuracy as well as rule economy.

With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect.

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance.

In this paper, the fuzzy neural network model is studied, the real-time regulation model of physical training intensity is analyzed and a real-time regulation system based on a fuzzy neural network is designed. The real-time, accurate and effective regulation of the physiological load intensity in the body of the exerciser is consistent with the predetermined goals of the training program. In this paper, we propose an RBF neural network, combined with the plan and demand of physical training operation situation sensing, and considering that most of the biological training operation data is fuzzy, this paper connects a fuzzy logic inference system and a neural network and proposes a network operation situation sensing model based on an RBF neural network structure. The RBF neural network and the traditional fuzzy neural network are compared. The experiments prove that this paper’s fuzzy neural network model has a faster training speed. In this paper, we use time-realistic control equipment to monitor the physical training process of athletes so that we can grasp the training situation of athletes in real-time and ensure that athletes can achieve better training results by changing training methods and changing training loads in time for those athletes who cannot reach their sports goals. In the process of physical fitness training monitoring, an effective monitoring of training, time-accurate regulation monitoring has the advantage of timely feedback on the training situation. This model has a better convergence effect during exercise and a higher accuracy of posture prediction during testing.

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises.

The coagulation cooling system is a common key component in many industrial processes, and reasonable temperature control is crucial. However, due to the complexity of the coagulation cooling system, traditional temperature control methods often cannot achieve optimal performance. To solve this problem, we design an intelligent temperature control decision model by a combination of genetic algorithm and fuzzy neural network. The study firstly utilizes genetic algorithm to optimize the objective function and constraint conditions of the coagulation cooling PID system. At the same time, fuzzy neural network is fused with genetic algorithm to establish a dedicated T-S/2 neural network structure, completing the complete model design of this study. Finally, cooling efficiency, task completion rate and model stability analysis are evaluated on real-world datasets. To validate the proposed model, an example of a coagulation cooling system was constructed in the laboratory and compared with traditional temperature control methods. The experimental results show that the proposal can significantly improve the performance of temperature control and reduce energy consumption under different conditions. In addition, the proposal has the characteristics of adaptability and optimization performance, and can effectively achieve optimal temperature control in uncertain and complex environments.

Pop-ups are a recently popular way of human-computer interaction, allowing viewers to actively participate in the discussion of a film video, but pop-ups can be deleted due to exceeding the size limit of the pop-up pool, and pop-up data is difficult to obtain in its entirety. To this end, this paper proposes a local convolutional neural network (CNN) based pop-up text sub-recognition algorithm. Finally, the fuzzed data is reconstructed into a data look-up table and the contents of the data look-up table are formatted for output. The results of this text classification and recognition algorithm are compared with those of four commonly used domestic and international search engines by subjective evaluation method, and the proposed text classification and recognition algorithm is found to be advanced.

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplane need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.

In order to predict the new real estate price, the factors affecting the real estate price were established by carrying out a survey of recently completed projects in the local environment. And a fuzzy neural network prediction model was developed. The model predicts the appropriate price for a new real estate by learning from historical data on the correlations between various factors that influence the prices of real estates and the actual selling prices. The experimental result shows that the fuzzy neural network prediction model has strong function approximation ability and is suitable for real estate price prediction depending on the quality of the available data.

In this paper, the energy value extracted from echo wave through wavelet transform is used as the feature of radar target, and fuzzy neural network is used as the classifier of target identification. The tested results show that the method have high classification ability.

In this paper, a new blind equalization algorithm based on fuzzy neural network (FNN) is proposed. It makes use of blind estimation (BE) and FNN classifier to equalize. Firstly BE algorithm is used to identify the channel character, the signals are rebuilt by deconvolution, and then the signals are classified by FNN classifier. This algorithm has the merits than the foregoing neural network algorithm, such as faster convergence speed, smaller residual error, lower bit error rate (BER), etc. The validity is proved by simulations.

This chapter introduces a new method for predicting chatter in milling process by a fuzzy neural network. Firstly, a milling experimental setup is built. And a set of the valuable experimental data is obtained under different tool wear states and cutting conditions. Secondly, since it is extremely difficult to construct an exact mathematical model for the setup, a fuzzy neural network model is proposed as a simplified one trained by using the experimental data. Thirdly, some simulation results are obtained based on the model. Finally, the further experiments are done to confirm the validity of predicting chatter in the model. The results show that chatter vibration in high-speed end milling could be exactly predicted via the model. Thus, the method described here is very effective to predict chatter in milling process.

In the temperature control, the object has a non-linear, strongly coupled system with large time delay and other characteristics, traditional PID can't achieve good results because the adjustment of the PID controller's parameter cannot be achieved. This paper presents an algorithm for optimization of PID controller's parameters based on fruit flies optimization algorithm, which can achieve optimal control by tuning PID controller's parameters. On this basis, we design a different regional temperature optimal control system, which makes pulse combustion furnace in different areas to achieve uniform temperature. The simulation results show that the organic combination of fruit flies optimization algorithm and the different regional temperature optimal control system improves the accuracy of temperature in furnace.

As a product of market economy, the stock market has its characteristics of high stakes and attractive benefits. But the fluctuation of stock price is always affected by many complexity factors. In this paper, China Merchants Bank stock is chosen as an example in utilizing good classification capability of BP neural network to forecast its future trend. In addition, the accuracy and stability of four kinds of neural networks in prediction of stock market trend are analyzed. Simulation experiments show that BP neural network has better performance than other approaches in learning capacity and generalization.

In the development process of modern power system, characteristics of the modern management of the operation management of power system is one of the power system load forecast, which Short time load forecasting accuracy is much more important. According to the analysis of the characteristics of neurological and fuzzy network, put forward a new method for constructing fuzzy neural network model, the BP network and the fuzzy inference process both together. Taking Shijiazhuang as an example, we have made an analysis and experiment of the short-term load forecasting of electric power system.

Domain adaptation addresses the problem of how to utilize a model trained in the source domain to make predictions for target domain when the distribution between two domains differs substantially and labeled data in target domain is costly to collect for retraining. Existed studies are incapable to handle the issue of information granularity, in this paper, we propose a new fuzzy domain adaptation method based on self-constructing fuzzy neural network. This approach models the transferred knowledge supporting the development of the current models granularly in the form of fuzzy sets and adapts the knowledge using fuzzy similarity measure to reduce prediction error in the target domain.