Identification Method of Sports Throwing Force Based on Fuzzy Neural Network

نویسنده

  • Rui Su
چکیده

In order to speed up the defects of the neural network computing and recognition, the essay proposes the information identification method research of sports throwing force based on the fuzzy neural network model. Firstly, I use the information, which is the combination of the wavelet transformation and the fuzzy neural network, to identify the new method combining and make the noise-suppressed processing of information. Then, according to the athlete’s throwing action and the extraction of signal processing characteristics, as well as the analysis of the fuzzy neural network algorithm. Finally, in order to verify the effectiveness of the proposed algorithm, I make analysis for the experimental results, which indicates that using this algorithm can not only have less noise than the traditional algorithm, but also have less number of the neural network computation. Besides, its recognition speed and accuracy is also higher.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

UAV attitude Sensor Fault Detection Based On Fuzzy Logic and by Neural Network Model Identification

Fault detection has always been important in aviation systems to prevent many accidents. This process is possible in different ways. In this paper, we first identify the longitudinal axis plane model using neural network approach. Then based on the obtained model and using fuzzy logic, the aircraft status sensor fault detection unit was designed. The simulation results show that the fault detec...

متن کامل

Utilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations

This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...

متن کامل

ESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS

In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...

متن کامل

Forecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange)

The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.To do so, first the prediction was made by neural network, then a series of price index was decomposed by w...

متن کامل

Image Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution

In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • JNW

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2013