Optimized Wavelet Neural Network Research Articles

Trajectory Tracking Control for Mobile Robot using Wavelet Network

This paper present a new wavelet network control scheme for mobile robot path tracking . The particles swarm optimization (PSO) method is used for determining the optimal wavelet neural network parameters and the proportional integral derivative (PID) controller parameters, for the control of nonholonomic mobile robot that involves trajectory tracking using two optimized WNN controllers one for speed control and the other for azimuth control. The mobile robot is modeled in Simulink and PSO algorithm is implemented using MATLAB. Simulation results show good performance for the proposed control scheme.

Fault identification of gearbox degradation with optimized wavelet neural network

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degra- dation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back- propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experi- mental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

An Obstacle Recognition Method Based on Improved PSO-WNN for Deicing Robot on Voltage Transmission Line

An intelligent obstacle recognition method based on improved particle swarm optimization wavelet neural network (IPSO-WNN) for deicing robot on voltage transmission line is presented because traditional obstacle recognition methods often have errors due to structural constraints. Firstly, the improved PSO algorithm is proposed by introduce idea of probabilistic leap in simulated annealing. Secondly , the improved algorithm is used to optimize the parameters of the wavelet network. Finally, the trained IPSO-WNN is used to recognize and classify the wavelet moments of the obstacle edged images of robot. Experimental results show that compared with PSO-WNN this method have quicker convergence rate and higher classification precision and improve obviously recognition function of obstacle on transmission line.

An Improved Decision Support System for Detection of Lesions in Mammograms Using Differential Evolution Optimized Wavelet Neural Network

In this paper, a computerized scheme for automatic detection of cancerous lesion in mammograms is examined. Breast lesions in mammograms are an area with an abnormality or alteration in the breast tissues. Diagnosis of these lesions at the early stage is a very difficult task as the cancerous lesions are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm - Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of tumor masses in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the abnormal breast tissues and normal breast tissues prior to classification. Then DEOWNN classifier is applied at the end to determine whether the given input data is normal or abnormal. The performance of the computerized decision support system is evaluated using a mini database from Mammographic Image Analysis Society (MIAS). The detection performance is evaluated using Receiver Operating Characteristic (ROC) curves. The result shows that the proposed algorithm has a sensitivity of 96.9% and specificity of 92.9%.

Improved Hybrid Particle Swarm Optimized Wavelet Neural Network for Modeling the Development of Fluid Dispensing for Electronic Packaging

An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for modeling the development of fluid dispensing for electronic packaging (MFD-EP) is presented in this paper. In modeling the fluid dispensing process, it is important to understand the process behavior as well as determine the optimum operating conditions of the process for a high-yield, low-cost, and robust operation. Modeling the fluid dispensing process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.