Wavelet Neural Network Model Research Articles

Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models

Summary Long-term drought forecasts can provide valuable information to help mitigate some of the consequences of drought. Data driven models are suitable forecast tools due to their minimal information requirements and rapid development times. This study compares the effectiveness of five data driven models for forecasting long-term (6 and 12 months lead time) drought conditions in the Awash River Basin of Ethiopia. The Standard Precipitation Index (SPI 12 and SPI 24) was forecasted using a traditional stochastic model (ARIMA) and compared to machine learning techniques such as artificial neural networks (ANNs), and support vector regression (SVR). In addition to these three model types, wavelet transforms were used to pre-process the inputs for ANN and SVR models to form WA-ANN and WA-SVR models; this is the first time that WA-SVR models have been explored and tested for long-term SPI forecasting. The performances of all models were compared using RMSE, MAE, R 2 and a measure of persistence. The forecast results indicate that the coupled wavelet neural network (WA-ANN) models were better than all the other models in this study for forecasting SPI 12 and SPI 24 values over lead times of 6 and 12 months in the Awash River Basin.

A k Nearest Neighbor based Local Linear Wavelet Neural Network Model for On-line Short-term Traffic Volume Prediction

This paper presents a forecasting method called k nearest neighbor based local linear wavelet neural network (kNN-LLWNN) for the on-line, short-term prediction of five-minute traffic volumes at westbound of Interstate 64 in Hampton Road in Virginia. The method is based on combining k nearest neighbor (k-NN), with local linear wavelet neural network (LLWNN). The idea is to apply k-NN method to form the training dataset for LLWNN instead of taking the whole historical dataset for training. The proposed model is compared with k-NN, LLWNN and support vector regression (SVR) separately from prediction accuracy and running time two aspects. Experiments are conducted to decide the most appropriate parameters for the four models for the verification dataset. For the test dataset, the study's findings appear to confirm the hypothesis that, kNN-LLWNN performs comparable with LLWNN and SVR, and its running time is much lower than LLWNN and SVR because of the introduction of k-NN.

An Improved Wavelet Neural Network Model for Evaluation of Corporate Performance

An Improved Wavelet Neural Network Model for Evaluation of Corporate Performance

Comparison of Estimation Capabilities of the Artificial Neural Network with the Wavelet Neural Network in Lipase‐Catalyzed Synthesis of Triethanolamine‐Based Esterquats Cationic Surfactant

AbstractIn this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.

Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting

Summary In this paper an attempt is made to show that the performance of daily river flow forecasting is improved when data-preprocessing techniques are used with computational intelligence methods. Especially for forecasting longer lead-times, one of the inherent problems in all forecasting methods is that the reliability of forecasting decreases with increasing the lead-time. Therefore wavelet multi-resolution analysis is coupled with artificial neural networks (ANN) and adaptive neuro-fuzzy interface system (ANFIS) which are two promising methods for forecasting nonlinear and non-stationary time series. Different models with a combination of the different input data sets are developed for 1, 2, 3, 4 and 5 days ahead forecasting in Harvey River, Western Australia. Daubechies and Symlet wavelets are used to decompose river flow time series to different levels. Hybrid wavelet neural networks (WNN) models are trained using Levenberg–Marquart (LM) algorithm and the wavelet neuro-fuzzy (WNF) models with subtractive clustering method to find the optimum number of fuzzy rules. Comparing the results with those of the original ANN and ANFIS models indicates that the hybrid models produce significantly better results, especially for the peak values and longer lead-times.

Deterministic prediction of wavelet neural network model and its application

Concerning the random prediction results of the neural network model,a compact wavelet neural network was constructed.The method transferred the wavelet function into the hidden layer of the Back-Propagation(BP) network and made use of a random certain state command to obtain the definite prediction results.Compared with the wavelet neural network realized by programming and BP network,this method is suitable for mass data training and has such advantages as strong adaptability and robustness for data samples,especially has better adaptability for high frequency stochastic time series,and has characteristics of determined predicted results,powerful practicability and so on.It can obviously improve the training speed,prediction accuracy and prediction efficiency of the model.Its efficiency has been proved by the gas emission prediction experiment of wavelet packet transformation and wavelet neural network.

Prediction Research of Highway Traveling Passenger Volume Based on Wavelet Neural Network

Aiming at the problem that it is difficult to predict the highway traveling passenger volume (HTPV), a new prediction model of HTPV based on wavelet neural network (WNN) is proposed. A case study is given to verify the proposed model. The simulation results show that the WNN model has higher convergence speed and prediction precision than the traditional BP neural network model (TBPNNM), and has more practical values.

The Predictive Control Method of VAV Air Conditioning System

Aiming at the characteristics which variable air volume air conditioning system is multi-variable, nonlinear and uncertain system, normal fuzzy neural network is hard to meet the requirements which dynamic control of multi-variable. In this paper, we put forward a recursive neural network predictive control strategy based on wavelet neural network model. Through recursive wavelet neural network predictor on line established controlled object’s mathematical model, and using Elman neural network controller on line corrected information we get, thus to improve control effect. The simulation results show that recursive wavelet neural network predictive control has stronger robustness and adaptive ability, high control precision, better and reliable control effect and other advantages. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3244

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

Optimization of cellulose phosphate synthesis from oil palm lignocellulosics using wavelet neural networks

Cellulose phosphate was synthesized from microcrystalline cellulose derived from oil palm lignocellulosics via the H3PO4/P2O5/Et3PO4/hexanol method. The influence of process variables (viz. temperature, reaction time, and the H3PO4/Et3PO4 ratio) on the properties of the resulting cellulose phosphate was investigated using a wavelet neural network model with the goals of ascertaining which factors were critical and of determining optimized reaction parameters for this synthesis. The experimental results corroborated the good fit of the wavelet neural network model. The prediction errors were quite small (less than 7%), and the regression values (R2 greater than 0.99) were also satisfactory.

Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting

AbstractThe present study demonstrates the capability of two preprocessing techniques such as wavelets and moving average (MA) methods in combination with feed-forward neural networks—namely, back propagation (BP) and radial basis (RB) and multiple linear regression (MLR) models—in the prediction of the daily inflow values of the Malaprabha reservoir in Belgaum, India. Daily data on 11 years of rainfall, inflow, and streamflow at an upstream gauging station have been used. The observed inputs are decomposed into subseries using discrete wavelet transform with different mother wavelet functions, and then the appropriate subseries is used as input to the neural networks for forecasting reservoir inflow. Model parameters are calibrated using 7 years of data, and the remaining data are used for model validation. More statistical indices have been used to determine the optimal models. Optimum architectures of the wavelet neural network (WNN) models are selected according to the obtained evaluation criteria in ...

A CLUSTERING-BASED FUZZY WAVELET NEURAL NETWORK MODEL FOR SHORT-TERM LOAD FORECASTING

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

Prediction of Rainfall in Da-Dong-Yong Hydrologic Station Based on Wavelet Neural Network

This paper aims to seek a way for improving rainfall prediction accuracy from the perspective of time unit points which are 5 days, 10 days, 15 days, 20 days, 25 days, and 30 days. Based on the daily rainfall data from 2001 to 2010 of Da-dong-yong hydrologic station, the rainfalls are predicted by establishing the model of wavelet neural network. Results show that prediction accuracy and stability of time unit points is 30-day > 25-day > 15-day > 10-day > 20-day > 5-day. The trend of six kinds of rainfall forecast is consistent. When the number of forecast data is fewer and time unit point is longer, the accuracy and stability of rainfall forecast are better.

Prediction Model of the Cement Strength Based on the Principal Component Wavelet Neural Network

In this paper, we study the integration of principal component analysis and wavelet neural net work to establish the principle component wavelet neural network model, and apply the model to predict the strength of cement, and using the average prediction error of the strength of cement by the model to compare with the results through the BP neural network model and the principal component neural network model, the average prediction error in this paper is the smallest. Therefore, the method in this paper has guiding significance to control the quality of the cement production.

An observer-based adaptive neural network tracking control of robotic systems

Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

ABSTRACTWe propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.

POME is treated for removal of color from biologically treated POME in fixed bed column: Applying wavelet neural network (WNN)

As Malaysia is one of the world's largest producer of palm oil, large amounts of palm oil mill effluent (POME) is generated. It was found that negatively charged components are accountable for POME color. An attempt was made to remove residual contaminants after conventional treatment using anion base resin. Adsorption experiments were carried out in fixed bed column. Various models such as the Thomas, the Yoon–Nelson, the Wolborska and BDST model were used to fit the experimental data. It was found that only the BDST model was fitted well at the initial breakthrough time. A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system. The results showed that the WNN model described breakthrough curves better than the commonly used models. The effects of pH, flow rate and bed depth on column performance were investigated. It was found that the highest uptake capacity was obtained at pH 3. The exhaustion time appeared to increase with increase in bed length and decrease in flow rate.

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.

Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod

Abstract In order to have effective agricultural production the impacts of drought must be mitigated. An important aspect of mitigating the impacts of drought is an effective method of forecasting future drought events. In this study, three methods of forecasting short-term drought for short lead times are explored in the Awash River Basin of Ethiopia. The Standardized Precipitation Index (SPI) was the drought index chosen to represent drought in the basin. The following machine learning techniques were explored in this study: artificial neural networks (ANNs), support vector regression (SVR), and coupled wavelet-ANNs, which pre-process input data using wavelet analysis (WA). The forecast results of all three methods were compared using two performance measures (RMSE and R2). The forecast results of this study indicate that the coupled wavelet neural network (WA-ANN) models were the most accurate models for forecasting SPI 3 (3-month SPI) and SPI 6 (6-month SPI) values over lead times of 1 and 3 months in the Awash River Basin in Ethiopia.

Research on the Dynamic Fitting and Prediction Model of Underground CBM Extraction Capacity Based on Improved Wavelet Neural Network Method

According to the difficult question that how to calculate the productivity of coal mine underground extraction-CBM, introduced the method of wavelet neural network, for different pre-draining time, the productivity of CBM was calculated with dynamic fitting. Because wavelet neural network exists convergence speed slow and easy to fall into local minimum, therefore a parameter correction of the improved algorithm was put forward. The first neuron function of output layer used hyperbolic tangent function instead of traditional Sigmod function, Secondly used the way that added the momentum term in adjustment type of weights to select the learning step, in order to improve the efficiency of network learning. Using the proposed improvement method, the productivity prediction model of coal mine underground extraction-CBM was established. The results showed that: the improved wavelet neural network model can be good for accurate prediction of the productivity model of coal mine underground extraction-CBM, the prediction accuracy and generalization ability was better than the BP neural network and the wavelet neural network model, it had important guiding significance to CBM extraction engineering of deployment and mine gas management.