Hybrid Wavelet Neural Network Research Articles

Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders

Using Hybrid Wavelet Approach and Neural Network Algorithm to Forecast Distribution Feeders

Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

Predicting water sorptivity coefficient in calcareous soils using a wavelet–neural network hybrid modeling approach

Sorptivity (S) coefficient is a measure of liquid absorption/desorption tendency of a porous medium by the capillary. It is a crucial component in hydrological modeling. Since its measurement is usually time-consuming and labor-intensive, the available data sets suffer from the lack of S coefficient. Therefore, its prediction through utilizing efficient numerical approaches (e.g., artificial neural networks, ANNs) has been received increased attention. For this purpose, the wavelet neural network (WNNs) with various wavelet types and decomposition levels was employed. The results were compared to that of two well-known NNs (multilayer perceptron, MLPNNs, and radial-basis function, RBFNNs) and multiple linear regression (MLR). Input attributes consisted of electrical conductivity, pH, initial water content, bulk density, mean weight diameter, geometric mean diameter, organic matter, and calcium carbonate equivalent of a 100-total data set. The performances of the approaches were demonstrated using the field data sets. Correlation-coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency coefficient (NSE) for test data set were 0.87, 0.015, 8.78, and 0.752 for RBFNNs; 0.92, 0.009, 7.19, and 0.798 for MLPNNs; 0.94, 0.005, 6.28, and 0.846 for WNNs, and 0.85, 0.097, 12.70, and 0.515 for MLR, respectively. The WNNs and in particular the ‘sym2′ wavelet function with decomposition level three was able to achieve accurate estimates. The NN models were ranked as RBF > MLP > WNN; WNN > MLP > RBF; MLP > WNN > RBF in terms of easy usability, accuracy and computational time, and cost-effectiveness, respectively. Regarding the predictions obtained, it can be concluded that the applied WNN model is a more efficient tool than the other models to predict S coefficient.

Predicting Cardiovascular Disease as a Long-Term Diabetes Complication using SOM

Cardiovascular disease (CVD) is the most common serious of long term type2 diabetic’s mellitus. It is estimated that most of the T2DM patients causes death due to CVD. Around 90% of CVD can be prevented with proper prediction of diabetes. Type 2 diabetes mellitus begins with insulin resistance, a condition in which it fails to respond to insulin properly. This paper explores Hybrid Wavelet Neural Network to train the system to learn the pattern to predict the disease and Self Organized map method is used for information clustering and visualization of excessive dimensional records to predict the disease with less parameter high accuracy which can help to prevent the disease. Modified Teaching Learning Based Optimization algorithm achieves the optimized learning from the pertained network. Teaching and learning based optimized technique yield better accuracy with a dataset of 770 patients. The measure of accuracy is compared with other algorithms and it is analyzed for further ratification.

A hybrid wavelet neural network (HWNN) for forecasting rainfall using temperature and climate indices

Rainfall forecasting plays an important role in water resources management and also for controlling the unusual events related to the rainfall. This study aims to forecast monthly rainfall from antecedent monthly rainfall, temperature and climate indices using a hybrid wavelet neural network (HWNN) model. The discrete wavelet transform is used incorporation with a conventional ANN model. The skilfulness of the proposed model is compared with the observed rainfall and the ANN model. The results show that the HWNN model provides a good fit with the observed rainfall data particularly in facing the extreme rainfall. The decomposed sub-series obtained by wavelet transform can extract invaluable information which is enormously useful for future rainfall prediction. The results confirm that the hybrid model considerably improves the neural network ability to predict future rainfall.

Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach

Accurate and reliable water temperature forecasting models can help in environmental impact assessment as well as in effective fisheries management in river systems. In this paper, a hybrid model that couples discrete wavelet transforms (WT) and artificial neural networks (ANN) is proposed for forecasting water temperature. Four mother wavelets, including Daubechies, Symlet, discrete Meyer and Haar, are considered to develop the WT-ANN hybrid model. The hybrid model is applied to forecast daily water temperature on the Warta River in Poland. Time series of daily water temperatures in eight river gauges as well as daily air temperatures of seven meteorological stations are used for forecasting daily water temperature. The performance of this WT-ANN hybrid model is evaluated by comparing the results with those obtained from linear and non-linear regression models as well as a traditional ANN model. The results show that the WT-ANN models perform well in simulating and forecasting river water temperature time series, and outperform the linear, non-linear and traditional ANN models. The superior performance of the WT-ANN models is particularly observed for extreme weather conditions, such as heat waves and drought. Among the four mother wavelets applied, the discrete Meyer performs the best, slightly better than the Daubechies at level 10 and Symlet, while the Haar mother wavelet has the lowest accuracy. In addition, the model performance improves with an increase in the decomposition level, indicating the importance of the choice of decomposition level. The outcomes of this study have important implications for water temperature forecasting and ecosystem management of rivers.

A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

Nowadays, using cloud computing services is becoming very popular among corporations and dependent users because of its flexibility and diverse facilities. So, demand for its infrastructures is increasing exponentially and with this huge growth of demands, it is getting more challenging for cloud providers to serve all the user requests in a way that quality of service is high enough and service level agreement is also met. Along years, researchers found out that for an optimized resource allocation which provides required user quality of service it is needed to know the amount of future workload in advance so resources can be prepared. There are many methods introduced for cloud workload prediction but excessive changes in the number of requests in cloud workloads cause a reduction in their prediction accuracy. To solve this problem, we have proposed a novel hybrid wavelet neural network method which can efficiently model excessive changes in workload and predict the upcoming requests. For training the proposed method accurately, we have used two heuristic algorithms, Artificial Immune System and Water Cycle Algorithm. The two mentioned heuristic algorithms are used for finding optimized parameters (such as bias and weight) for the wavelet neural network algorithm. The proposed wavelet neural network trained with heuristic algorithms is tested on real and standard cloud workloads. At last, the accuracy of the proposed method is thoroughly surveyed statistically and also compared with other state-of-the-art prediction methods. Simulation results show that our method improves MAPE error at least 10% in comparison to other rival prediction methods.

Hybrid Wavelet Neural Network Approach for Daily Inflow Forecasting Using Tropical Rainfall Measuring Mission Data

AbstractA novel wavelet-artificial neural network hybrid model (WA-ANN) for short-term daily inflow forecasting is proposed, using for the first time Tropical Rainfall Measuring Mission (TRMM) data...

A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test).

A Preliminary Study on a Hybrid Wavelet Neural Network Model for Forecasting Monthly Rainfall

In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting rainfall with the data of antecedent monthly rainfalls, the ant colony optimization algorithm (ACO) is combined with particle swarm optimization algorithm (PSO) to improve performance of artificial neural network (ANN) model. ACO is adopted to initialize the network connection the weights of and thresholds of WNN and PSO is used to update the parameters of ACO, HWNN can avoid falling into a local optimal solution and improve its convergence rate and obtain more accurate results. In simulations based on monthly rainfall data from the city of Ningde in the southeastern China. The forecasting performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error, root mean square error and average absolute percentage error. The results show that the HWNN model improves the monthly rainfall forecasting accuracy over Ningde in comparison to the reference models. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.

Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication.

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.

Wavelet neural network–based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power

Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this article is to develop a hybrid wavelet neural network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this article. This article proposes shuffled frog leap algorithm for solving economic dispatch problem in power systems. The non-linear characteristics of the generator such as prohibited operating zone and non-smooth functions are considered. The feasibility of the proposed algorithm is demonstrated for 5 units, 6 units and 15 units systems and it is compared with the existing solution techniques. The results show that the proposed algorithm is indeed capable of handling economic dispatch problems.

Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting

Electricity forecasting plays an important role in the operation of electrical power systems. Many models have been developed to obtain accurate forecasting results, but most of them focus more on a single forecasting indicator, such as short-term load forecasting (STLF), short-term wind speed forecasting (STWSF) or short-term electricity price forecasting (STEPF). In this paper a new hybrid model based on the singular spectrum analysis (SSA) and modified wavelet neural network (WNN) is proposed for all the short-term load forecasting, short-term wind speed forecasting and short-term electricity price forecasting. In this model, a new improved cuckoo search (CS) algorithm is proposed to optimize the initial weights and the parameters of dilation and translation in WNN. Case studies of half-hourly electrical load data, 10-min-ahead wind speed data and half-hourly electricity price data are applied as illustrative examples to evaluate the proposed hybrid model, respectively. Experiments show that the hybrid model resulted in 46.4235%, 31.6268% and 25.8776% reduction in the mean absolute percentage error compared to the comparison models in short-term load forecasting, short-term wind speed forecasting and short-term electricity price forecasting, respectively.

A New Hybrid Wavelet Neural Network and Interactive Honey Bee Matting Optimization Based on Islanding Detection

In this paper a passive Neuro-wavelet on the basis of islanding detection procedure for grid-connected inverter-based distributed generation has been developed. Moreover, the weight parameters of neural network are optimized by Interactive Honey Bee Matting optimization (IHBMO) to increase the efficiency of the capability of suggested procedure in tendered problem. Islanding is the situation where the distribution system including both distributed generator and loads is disconnected from the major grid as a consequence of lots of reasons such as electrical faults and their subsequent switching incidents, equipment failure, or pre-planned switching events like maintenance. The suggested method uses and combines wavelet analysis and artificial neural network together to detect islanding. It can be used in removing discriminative characteristics from the acquired voltage signals. In passive schemes have a large Non Detection Zone (NDZ), concern has been raised on active method because of its lowering power quality impact. The main focus of the proposed scheme is to decrease the NDZ to as close as possible and to retain the output power quality fixed. The simulations results, performed by MATLAB/Simulink, demonstrate that the mentioned procedure has a small non-detection zone. What is more, this method is capable of detecting islanding precisely within the least possible amount of standard time.

Wavelet Neural Network-based Wind-power Forecasting in Economic Dispatch: A Differential Evolution, Bacterial Foraging Technology, and Primal Dual-interior Point Approach

Wind speed and wind-power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this paper is to develop a hybrid wavelet neural network model for wind-speed forecasting and thus, in turn, for wind-power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this paper. The solution methods, namely primal dual interior point, differential evolution, and bacterial foraging technology, are used for solving the wind-thermal economic dispatch (ED). The feasibility of the proposed algorithms is demonstrated on three-unit, 13-unit, and 40-unit systems and their performances are compared in terms of the generation cost and execution time. The results show that the proposed algorithms are indeed capable of handling ED problems.

Wavelet Neural Network Based Wind Speed Forecasting and Wind Power Incorporated Economic Dispatch with Losses

Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this paper is to develop a hybrid Wavelet Neural Network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this paper. Three solution techniques, viz., Primal Dual Interior Point, Differential Evolution and Bacterial Foraging algorithms have been employed for optimal scheduling and tested utilizing the IEEE 118 bus system. A realistic example case, Indian utility 66 bus system is also presented. Using the simulation results, the performance of the methods proposed are compared and analyzed.

Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model.

The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet-neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R (2)) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.

A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall

In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting monthly rainfall from antecedent monthly rainfall and climate indices by incorporating the multiresolution analysis (MRA), mutual information (MI) and particle swarm optimization (PSO) into artificial neural network (ANN) models. The standardized monthly rainfall anomaly and large-scale climate indices are first decomposed by using the maximal overlap discrete wavelet transform (MODWT) into a certain number of subseries components with different time scales. Then at each time scale, a small and appropriate subset of ANN inputs is identified by the modified partial MI algorithm from a set of candidate subseries components with different lags for forecasting the rainfall anomaly subseries at the corresponding scale. The optimal number of neurons in the hidden layers of ANNs is determined by PSO algorithm, and then each of rainfall anomaly subseries is forecasted from the selected predictor subseries. Finally, the monthly rainfall forecast is achieved by summing all the predicted anomaly subseries and applying the inverse transform of standardized monthly rainfall. The proposed HWNN method is examined with 255 rain gauge stations over Australia, and compared to the reference methods based on the undecomposed time series. The forecasting performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and Nash–Sutcliffe efficiency. The results show that the HWNN model improves the monthly rainfall forecasting accuracy over Australia in comparison to the reference models, and the improvement is more significant for the inland stations in southeast Australia and stations in west Australia.

A hybrid Wavelet Neural Network and Switching Particle Swarm Optimization algorithm for face direction recognition

Face direction recognition is an important research issue in human–computer interaction. In order to improve recognition accuracy, a novel hybrid approach called Switching Particle Swarm Optimization–Wavelet Neural Network (SPSO–WNN) is presented. In this model, we employ the recently proposed Switching Particle Swarm Optimization (SPSO) algorithm to optimize the parameters of weights, scale factors, translation factors and threshold in Wavelet Neural Network (WNN). The proposed SPSO–WNN method has fast convergence speed and higher learning ability than conventional WNNs. Especially, a mode-dependent velocity updating equation with Markovian switching parameters is introduced in SPSO to overcome the contradiction between the local search and the global search, which makes it easy to jump the local minimum. The experiment results of the recognition for face direction show the feasibility and effectiveness of the proposed method. Compared with Particle Swarm Optimization–Wavelet Neural Network (PSO–WNN), Genetic Algorithm–Wavelet Neural Network (GA–WNN) and WNN, the proposed method has much better performance over them.

Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques

AbstractOne of the key elements in achieving sustainable water resources and environmental management is forecasting the future condition of the surface water resources. In this study, the performance of a river flow forecasting model is improved when different input combinations and signal processing techniques are applied on multi‐layer backpropagation neural networks. Haar, Coiflet and Daubechies wavelet analysis are coupled with backpropagation neural networks model to develop hybrid wavelet neural networks models. Different models with different input selections and structures are developed for daily, weekly and monthly river flow forecasting in Ellen Brook River, Western Australia. Comparison of the performance of the hybrid approach with that of the original neural networks indicates that the hybrid models produce significantly better results. The improvement is more substantial for peak values and longer‐term forecasting, in which the Nash–Sutcliffe coefficient of efficiency for monthly river flow forecasting is improved from 0.63 to 0.89 in this study. Copyright © 2014 John Wiley & Sons, Ltd.