Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit

Incorporating intelligence into exit choice model for typical evacuation

Integrating an exit choice model into a microscopic crowd dynamics model is an essential approach for obtaining more efficient evacuation model. We describe various aspects of decision-making capability of an existing rule-based exit choice model for evacuation processes. In simulations, however, the simulated evacuees clogging at exits have behaved non-intelligently, namely they do not give up their exits for better ones for safer egress. We refine the model to endow the individuals with the ability to leave their exits due to dynamic changes by modifying the model of their excitement resulted from the source of panic. This facilitates the approximately equal crowd size at exits for being until the end of the evacuation process, and thereby the model accomplishes more optimal evacuation. For further intelligence, we introduce the prediction factor that enables higher probability of equally distributing evacuees at exits. A simulation to validate the contribution is performed, and the results are analyzed and compared with the original model

Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, two different approaches were proposed for improving the predictive capability of WNNs. First, the types of activation functions used in the hidden layer of the WNN were varied. Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. The modified WNN was then applied to heterogeneous cancer classification using four different microarray benchmark datasets. The comparative experimental results showed that the proposed methodology achieved an almost 100% classification accuracy in multiclass cancer prediction, leading to superior performance with respect to other clustering algorithms. Subsequently, performance comparisons with other classifiers were made. An assessment analysis showed that this proposed approach outperformed most of the other classifiers

An effective and novel wavelet neural network approach in classifying type 2 diabetics

Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved

Kernelized radial basis probabilistic neural network for classification of river water quality

Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields.In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of squares distance.The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space.Through comparing the four constructed classification models with Kernelized RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as intended, results showed that, model classification on River water quality of Langat river in Selangor, Malaysia by Kernelized RBPNN exhibited excellent performance in this regard

Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm

Algoritma perambatan balik telah terbukti sebagai salah satu algoritma rangkaian neural yang paling berjaya. Namun demikian, seperti kebanyakan kaedah pengoptimuman yang berasaskan kecerunan, ianya menumpu dengan lamb at dan keupayaannya berkurangan bagi tugas-tugas yang lebih besar dan kompleks. Dalam tesis ini, faktor-faktor yang menguasai kepantasan pembelajaran algoritma perambatan balik diselidik dan dianalisa secara matematik untuk membangunkan strategi-strategi bagi memperbaiki prestasi algoritma pembelajaran rangkaian neural ini. Faktor-faktor ini meliputi pilihan pemberat awal, pilihan fungsi pengaktifan dan nilai sasaran serta dua parameter perambatan, iaitu kadar pembelajaran dan faktor momentum. The backpropagation algorithm has proven to be one of the most successful neural network learning algorithms. However, as with many gradient based optimization methods, it converges slowly and it scales up poorly as tasks become larger and more complex. In this thesis, factors that govern the learning speed of the backpropagation algorithm are investigated and mathematically analyzed in order to develop strategies to improve the performance of this neural network learning algorithm. These factors include the choice of initial weights, the choice of activation function and target values, and the two backpropagation parameters, the learning rate and the momentum factor

Optimization of cellulose phosphate synthesis from oil palmlignocellulosics using wavelet neural networks

Cellulose phosphate was synthesized from microcrystalline cellulose derived from oil palm lignocellu-losics via the H3PO4/P2O5/Et3PO4/hexanol method. The influence of process variables (viz. temperature,reaction time, and the H3PO4/Et3PO4ratio) on the properties of the resulting cellulose phosphate wasinvestigated using a wavelet neural network model with the goals of ascertaining which factors werecritical and of determining optimized reaction parameters for this synthesis. The experimental resultscorroborated the good fit of the wavelet neural network model. The prediction errors were quite small(less than 7%), and the regression values (R2greater than 0.99) were also satisfactory

A harmony search-based learning algorithm for epileptic seizure prediction

The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. Apart from the traditional gradient descent-based approach, metaheuristic algorithms can also be used to determine these parameters. In this work, the harmony search algorithm is employed to find the optimal solution for both synaptic weight values and bias terms in the learning of wavelet neural network. The standard harmony search algorithm is modified accordingly in the aspect of initialization of harmony memory, as well as during the improvisation stage. The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms in terms of sensitivity

Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then applied in approximating a benchmark piecewise function. Subsequently, performance comparisons with other developed methods in studying the same benchmark function were made. An assessment analysis showed that this proposed approach outperformed the rest. The efficiency of the modified WNNs was explored through a real-world application problem-specifically, the prediction of time-series pollution data at Texas of United States. The comparative experimental results showed that integrating different wavelet families into the hidden layer of WNNs leads to superior performanc

Reliable epileptic seizure detection using an improved wavelet neural network

Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts