RBFN Model Research Articles

Prediction of Ground Water Level using SVM-WOA Approach: A Case Study

Reliable and accurate estimation of Groundwater Level (GWL) fluctuations is essential and vital for sustainable water resources management. Due to uncertainties and interdependencies in hydro-geological processes, GWL prediction is complex by the fact that fluctuation of GWL is extremely nonlinear and non-stationary. Utilising novel methods for accurately predicting GWL is of vital significance in arid regions. In present work, Support Vector Machine (SVM), in combination with Whale Optimisation Algorithm (SVM-WOA), is applied to forecast GWL in Bhubaneswar region (Odisha University of Agricultural Technology). Three quantitative statistical performance assessment indices, coefficient of determination (R2), Mean Squared Error (MSE), and Wilmott Index (WI), is used to assess model performances. Based on the assessment with conventional SVM and RBFN models, the performance of hybrid SVM-WOA model is preeminent. SVM-WOA is capable of predicting nonlinear behavior of GWLs. Proposed modelling technique can be applied in different regions for proper management of groundwater resources and provides significant information, at a short time scale, to estimate variability in groundwater at local level.

Inspection process for dimensioning through images and fuzzy logic

Abstract This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN.

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

A Machine Learning Approach for Speech Detection in Modern Wireless Communication Environment

Modern wireless communication has gained a improved position as compared to previous time. Similarly, speech communication is the major focus area of research in respective applications. Many developments are done in this field. In this work, we have chosen the OFDM modulation based communication system, as it has importance in both licensed and unlicensed wireless communication platform. The voice signal is passed though the proposed model to obtain at the receiver end. Due to different circumstances, the signal may be corrupted partially at the user end. Authors try to achieve a better signal for reception using a neural network model of RBFN. The parameters are chosen for the RBFN model, as energy, ZCR, ACF, and fundamental frequency of the speech signal. In one part these parameters have eligibility to eliminate noise partially, where as in other part the RBFN model with these parameters proves its efficacy for both noisy speech signals with noisy channel as Gaussian channel. The efficiency of OFDM model is verified in terms of symbol error rate and the transmitted speech signal is evaluated in term of SNR that shows the reduction of noise. For visual inspection, a sample of signal, noisy signal and received signal is also shown. The experiment is performed with 5dB, 10dB, 15dB noise levels. The result proves the performance of RBFN model as the filter.The performance is measured as the listener’s voice in each condition. The results show that, at the time of the voice in noise environment, proposed technique improves the intelligibility on speech quality.

Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation

Dam health monitoring is usually achieved by using the hydrostatic-season-time model that simulates the temperature effect through harmonic sinusoidal functions. The model is convenient, but does not take into account the effect of temperature variations in different years on dam response. This paper presents a dam health monitoring model using long-term air temperature for thermal effect simulation. In the model, harmonic sinusoidal functions are replaced by long-term air temperature to simulate the temperature effect on the dam response. The machine learning technique RBFNis adopted to mine the temperature effect from long-term air temperature series. The proposed dam health monitoring model was verified using monitoring data of a real concrete gravity dam. Results show that the nonlinear RBFN model utilizing long-term air temperature achieves better results than the model using harmonic sinusoidal functions.

Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models

In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chla, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chla, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.

Online 24-h solar power forecasting based on weather type classification using artificial neural network

► The RBFN model can forecast PV power output accurately in sunny days and cloudy days. ► The RBFN model makes a direct forecast of the power output of the PV system . ► A self-organized map is applied to classify the local weather type. ► The main factors are identified to forecast the power output of the PV system. Power forecasting is an important factor for planning the operations of photovoltaic (PV) system. This paper presents an advanced statistical method for solar power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of solar irradiance, relative humidity and temperature at the site of the photovoltaic power system. A self-organized map (SOM) is trained to classify the local weather type of 24 h ahead provided by the online meteorological services. A unique feature of the method is that following a preliminary weather type classification, the neural networks can be well trained to improve the forecast accuracy. The proposed method is suitable for operational planning of transmission system operator, i.e. forecasting horizon of 24 h ahead and for PV power system operators trading in electricity markets. Application of the forecasting method on the power production of an actual PV power system shows the validity of the method.

Comparison of ARIMA and RBFN Models to Predict the Bank Transactions

Comparison of ARIMA and RBFN Models to Predict the Bank Transactions

A Localized Forgetting Method for Gaussian RBFN Model Adaptation

In this paper a localized forgetting method is proposed for on-line adaptation of Gaussian radial basis function network models. It is realised that the commonly used exponential forgetting applies to the past data from the entire operating space uniformly and therefore, is not correct for nonlinear systems where dynamics are different in different operating regions. The new method proposed in this paper sets different forgetting factors in different regions according to the response of the local centre to the current measurement data. The method is applied in conjunction with the recursive orthogonal Least Squares algorithm and the computing is consequently very efficient. The developed method is applied to modelling of dissolved oxygen in a chemical reactor rig. It shows a smaller mean squared error for one-step-ahead prediction than using the uniform forgetting.

RBFN Model Adaptation Based on Orthogonal Decomposition

An algorithm is developed in this paper to adapt a radial basis function network model on-line. This includes updating the output layer weights as well as the number of centres by pruning the centres contributing least to the network output and adding new centres when the modelling error is greater than a given criterion. The adaptation is based on the manipulation of a triangular matrix obtained in orthogonal decomposition of the hidden layer output matrix. The algorithm can be applied to modelling time varying dynamics of an unknown non-linear process and therefore is particularly useful in adaptive control. Simulations on a multivariate process demonstrate the effectiveness of the method.