Application Of Wavelet Neural Network Research Articles

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

Abstract Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.

Application of wavelet neural network for dynamic analysis of moment resisting frames

Application of wavelet neural network for dynamic analysis of moment resisting frames

Research and application of wavelet neural network in electrical resistivity imaging inversion

In order to obtain high-quality electrical resistivity imaging (ERI) inversion results, this paper proposes a wavelet-based neural network inversion method. In addition, it is applied to 2D electrical resistivity tomography imaging. Firstly, a sample set suitable for wavelet neural network training is designed to optimize neural network parameters and improve the accuracy of electrical resistivity inversion. Secondly, a hybrid multi-layer wavelet neural network is designed, which uses Mexican-hat and Morlet wavelets as activation functions for different hidden layers, to improve the generalization ability and stability of the network. Finally, the superiority of the WNN is verified through two experiments and applied for inversion in field examples. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.

Application of Wavelet Neural Network in Building Settlement Prediction

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

Application of Wavelet Neural Network in Forward Kinematics Solution of 6-RSU Co-axial Parallel Mechanism Based on Final Prediction Error

Application of artificial neural network (ANN) in forward kinematic solution (FKS) of a novel co-axial parallel mechanism with six degrees of freedom (6-DOF) is addressed in Current work. The mechanism is known as six revolute-spherical-universal (RSU) and constructed by 6-RSU co-axial kinematic chains in parallel form. First, applying geometrical analysis and vectorial principles the kinematic model is extracted and inverse kinematics solution is done. Due to highly nonlinear characteristic of the model, forward kinematic solution for 6-RSU is so complicated. Therefore, ANN based on wavelet analysis, as a powerful solution, is designed and applied to solve FK problem. The minimum prediction risk principle with using final prediction error (FPE) is applied to find the best and optimum topology of our proposed neural network (WNN) in this paper. Furthermore, proposed wavelet WNN is developed to approximate the specific reference trajectories for manipulated platform of mechanism and the results are obtained. Comparing the extracted results by WNN with closed form solution (CFS) demonstrates the accuracy and efficiency of the proposed WNN.

Application of Wavelet Neural Networks for Monitoring of Extraction of Carbon Multi-Functional Medical Nano-Agents from the Body

In the given study the new approach to solution of the problem of monitoring the removal of luminescent nanocomposites and their components from the body with urine is proposed. The monitoring is performed by luminescence spectra with the help of classical perceptron type neural networks and of wavelet neural networks. A comparative analysis of the results obtained with application of multilayer perceptrons and wavelet neural networks is carried out.

The Application of Wavelet Neural Network in the Settlement Monitoring of Subway

The settlement monitoring of subway runs through the entire construction stage of subway. It is very important to predict the accurate settlement value for construction safety of subway. In this paper, the wavelet transform is used to denoise the settlement data. The auxiliary wavelet neural network, embedded wavelet neural network and single BP neural network are applied to predict the settlement of Tianjin subway. Compared with single BP neural network and auxiliary wavelet neural network, the embedded wavelet neural network model has a higher accuracy and better prediction effect. The embedded wavelet neural network is more valuable than the BP neural network model so it can be used in the prediction of subway settlement

The Application of Wavelet Neural Network in Prediction of the Fish Price

The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision

Analysis on Application of Wavelet Neural Network in Wind Electricity Power Prediction

Wind electricity power has fluctuation, and accurate and reasonable wind electricity power prediction is very important for solving wind electricity network and combination. This paper takes an analysis of a lot of actual data of a certain wind electricity field. Through wavelet neural network and time series method rolling, it can predict the overall power of wind electricity field. The result shows that for the original data of sampling time length and large sampling frequency, the model constructed by this paper has very good prediction effect. Because of the fan installation position, wind electricity fan flow effect and other random factor influence, wind electricity field overall power and single unit power distribution have difference. Through comparing with the time series parameters, it puts forward that single wind electricity unit power has smooth effect for overall power of wind electricity field. Finally, it summarizes the prediction effect and puts forward some reasonable suggestions for wind electricity network problems.

Research on a Kind of Deformation Prediction Model of Wavelet Neural Network

Combining with the localized analysis capability of the wavelet and the learning capability of the neural network, this article investigates the wavelet neural network nonlinear time series model and the wavelet neural network architecture integrated model which combines the affine transformation and the rotation transformation ,moreover, based on wavelet neural network algorithm that substitutes the Morlet wavelet function for the Sigmoid function, researchers analyze the application of Wavelet Neural Network in settlement deformation of high-speed rail. The example this paper discussed shows that the wavelet neural network has good results in the deformation prediction, especially in large complicated engineering.

Research on the Application of Wavelet Neural Network in Temperature Control System

A rapid learning algorithm was put forward to realize complex system modeling and self-adaptive control with uncertainty, high nonlinear and lame time-delay. Merits of internal model control were combined, such as simple design, food regulation capacity, high robustness and the ability to eliminate the unknown disturbance to construct a internal model control system based on wavelets neural network, which was characterized by high robustness and quick response speed and then it can brim food control performance when controlled objects vary in a wide range. Finally, it was triumphantly used in simulation of the superheated steam temperature reduction control system of 500 MW unit and food performances are obtained.

Application of Differential Evolution Wavelet Neural Networks for State Recognition of Tool Wear

Tool wear monitor is one of the critical issue in industry, accurate prediction of tool life can guarantee surface quality, the new method of tool wear stated recognition was proposed with application of wavelet neural networks (WNN). An application of differential evolution (DE) algorithm was introduced to training artificial neural networks. The three layer wavelet neural network was constructed and optimized with differential evolution algorithm. Cutting force and cutting noise was monitored and the signal was processed as training samples for wavelet neural networks. Simulation with BPNN, WNN and DEWNN to show the new method can avoid normal neural networks with convergence quality and enhance learning speed, also the diagnosis precision and efficiency was improved.

Dynamic prediction of gas emission based on wavelet neural network toolbox

This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network (WNN) toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.

Application of wavelet neural network on thermal load forecasting

In order to improve operational efficiency of district-heating systems, it is necessary for the energy companies to have reliable optimisation routines to realise the intelligent processing of the heating system. However, before a production plan for the heat-producing units can be constructed, a prediction of the heat demand firstly needs to be determined. The outdoor temperature together with the characteristic of heating system has the greatest influence on the heat supply. This is also the core of the load prediction model developed in the paper. A dynamic model of heating system is presented, in which wavelet analysis is combined with neural network on the basis of the analysis of many factors. The model is trained and tested with measured values of 128 days from a heat producer. The results from this simulation were then compared with results from alternative methods found in literatures. It is shown that the approach has more satisfactory tracking performance and higher accuracy than the other two algorithms. It is applicable for short-term thermal load forecast.

The Application of Wavelet Neural Networks on Rainfall Forecast

We proposed the improved wavelet neural network and applied it to forecast rainfall. Activation function was selected automatically from three wavelet basis functions. The three given wavelet basis functions were Morlet, Mexican-Hat and Different Of Gauss(DOG) wavelet basis functions respectively. We demonstrat its effectiveness by forecasts rainfall of some cities in China. The results show that the improved wavelet networks method is better than BP neural network method in rainfall forecast.

Application of Wavelet Neural Network into the Sustainable Development of Power Market

Application of Wavelet Neural Network into the Sustainable Development of Power Market

Application of WNN in Ring Ball Gratings Torque Sensor’s Signal Demodulation

The components structure and principle of ring ball gratings sensor is put forward for the present state that there still has not a better method to detect torque under the extreme environment in domestic. According to the localization property in time and frequency domains and the translation invariant property, the preprocessed signal of sensor is decomposed with multi resolution, and the signal is reconstructed with wavelet under the intervention of neural network. The wavelet neural networks (WNN) model is constructed and the algorithm routine is realized in laboratory virtual instrument engineering workbench (LabVIEW). With the testing device, the demodulation experiments are carried out for the corresponding sensor signals when the shaft is continuous rotated along the same direction, rotated along the alternate direction and knocked. The results indicate that the demodulation effect and confidence level are very well.

Research and Application of Wavelet Neural Networks of Particle Swarm Optimization Algorithm in the Performance Prediction of Centrifugal Compressor

The traditional method of centrifugal compressor performance prediction is usually the BP neural network, however, the problems are that prediction accuracy is not high enough, convergence is slow and it is apt to fall into local optimal solution. In order to predict the performance of centrifugal compressors more accurately and identify the implicit problems in advance, now we combine the particle swarm optimization, wavelet theory and neural networks, to establish performance prediction model of centrifugal compressor based on wavelet neural network of PSO. First, set the various parameters of wavelet neural network as the particle position vector X and the energy function of mean square error as the optimized objective function. By particle swarm optimization algorithm to iterate the basic formula to obtain the corresponding WNN coefficient and then use back-propagation algorithm to train WNN to approach any nonlinear function. Simulation results show that application of the prediction model can achieve the accurate prediction of performance and monitoring of centrifugal compressor. The prediction model has the advantages of simple algorithm, stable structure, fast calculation of convergence speed and strong generalization ability with a prediction accuracy of 99%, 13% higher than prediction accuracy of traditional methods, which has a certain theoretical research value and practical value.

Application of Wavelet Neural Network in Power Load Forecasting

This paper presents a power load prediction mathematic model based on wavelet neural network theory which is a new neural network model thrives from wavelet analysis theory. By applying wavelet analysis, this paper can analyze power load in any dimension and collect it effectively. At the end of this paper, the authors analyze the model parameters and discuss various factors deeply.

Application of wavelet neural network in signal processing of MEMS accelerometers

A new method with wavelet neural network is described to optimize MEMS accelerometers for temperature independent sensitivity. Linear accelerations are measured and compensated by a thermocouple the fast algorithm which is used to deal with the nonlinearity error. The simulation results show that MEMS accelerometers with compensation is characterized by an excellent temperature stability of the sensitivity with less than 0.1% variation for a temperature range −40–100°C, while the variation of acceleration without compensation is 8%. The proposed algorithm can be useful for realization of high accuracy miniature gyroscope systems based on MEMS technology.