Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction

U radu je predložen i prikazan ansambl neuronskih mreža za predviđanje potrošnje toplote univerzitetskog kampusa. Za obučavanje i testiranje modela korišćeni su eksperimentalni podaci. Razmatrano je poboljšanje tačnosti predviđanja primenom k-means metode klasterizacije za generisanje obučavajućih podskupova neuronskih mreža zasnovanih na radijalnim bazisnim funkcijama. Korišćen je različit broj klastera, od 2-5. Izlazi članova ansambla su kombinovani primenom aritmetičkog, težinskog i osrednjavanja metodom medijane. Pokazano je da ansambli neuronskih mreža ostvaruju bolje rezultate predviđanja nego svaka pojedinačna mreža članica ansambla. PR Data used for this paper were gathered during study visit to NTNU, as a part of the collaborative project: Sustainable energy and environment in Western Balkans.For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for training and testing the models. Improvement of the prediction accuracy using k-means clustering for creating subsets used to train individual radial basis function neural networks is examined. Number of clusters is varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. It is shown that ensembles achieve better prediction results than the individual network

A performance evaluation of pruning effects on hybrid neural network

In this paper, we explore the pruning effects on a hybrid mode sequential learning algorithmnamely FuzzyARTMAP-prunable Radial Basis Function (FAM-PRBF) that utilizes FuzzyARTMAP to learn a training dataset and Radial Basis Function Network (RBFN) to performregression and classification. The pruning algorithm is used to optimize the hidden layer ofthe RBFN. The experimental results show that FAM-PRBF has successfully reduced thecomplexity and computation time of the neural network.Keywords: pruning; radial basis function network; fuzzy ARTMAP

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[conferencetype]]國際[[conferencedate]]20120918~20120921[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120918~20120922[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Japan,Toky

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

Adaptive structure radial basis function network model for processes with operating region migration

An adaptive structure radial basis function (RBF) network model is proposed in this paper to model nonlinear processes with operating region migration. The recursive orthogonal least squares algorithm is adopted to select new centers on-line, as well as to train the network weights. Based on the R matrix in the orthogonal decomposition, an initial center bank is formed and updated in each sample period. A new learning strategy is proposed to gain information from the new data for network structure adaptation. A center grouping algorithm is also developed to divide the centers into active and non-active groups, so that a structure with a smaller size is maintained in the final network model. The proposed RBF model is evaluated and compared to the two fixed-structure RBF networks by modeling a nonlinear time-varying numerical example. The results demonstrate that the proposed adaptive structure model is capable of adapting its structure to fit the operating region of the process effectively with a more compact structure and it significantly outperforms the two fixed structure RBF models

Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme

Empirical analysis of classifiers and feature selection techniques on mobile phone data activities

Mobile phones nowadays become ubiquitous device and not only a device to facilitate communication, with some addition feature of hardware and software.There are many activities can be captured using mobile phone with many of features.However, not all of these features could benefit to the in processing and analyzer.The large number of features, in some cases, gives less accuracy influence the result. In the same time, a large feature takes requires longer time to build model. This paper aims to analyze accuracy impact of selected feature selection techniques and classifiers that taken on mobile phone activity data and evaluate the method. Furthermore, with use feature selection and discussed emphasis on accuracy impact on classified data of respective classifier, usage of features can be determined. To find the suitable combination between the classifier and the feature selection sometime is crucial. A series of tests conducted in Weka on the accuracy on feature selection shows a consistency on the results although with different order of features.The result found that combination of K* algorithm and correlation feature selection is the best combination with high accuracy rate and in the same time produce less feature subset

Empirical analysis of classifiers and feature selection techniques on mobile phone data activities

Mobile phones nowadays become ubiquitous device and not only a device to facilitate communication, with some addition feature of hardware and software.There are many activities can be captured using mobile phone with many of features.However, not all of these features could benefit to the in processing and analyzer.The large number of features, in some cases, gives less accuracy influence the result. In the same time, a large feature takes requires longer time to build model. This paper aims to analyze accuracy impact of selected feature selection techniques and classifiers that taken on mobile phone activity data and evaluate the method. Furthermore, with use feature selection and discussed emphasis on accuracy impact on classified data of respective classifier, usage of features can be determined. To find the suitable combination between the classifier and the feature selection sometime is crucial. A series of tests conducted in Weka on the accuracy on feature selection shows a consistency on the results although with different order of features.The result found that combination of K* algorithm and correlation feature selection is the best combination with high accuracy rate and in the same time produce less feature subset