International Journal of Computational Intelligence and Applications

Recombination in Genetic Algorithms (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations, hopefully producing an offspring that has the good characteristics of both parents, and this requires explicit chromosome and recombination, operator by design. This paper presents a novel evolutionary approach based on symbiogenesis which uses symbiotic combination instead of sexual recombination, and by using this operator, it requires no domain knowledge for chromosome or combination operator design. The algorithm is benchmarked on three problem sets: combinatorial optimization category, deceptive problems, and fully deceptive problems. The results, compared with that of standard genetic algorithm and symbiotic evolutionary adaptation model, show higher success rates and faster results.

Fixed order neural networks (FONN), such as high order neural network (HONN), in which its architecture is developed from zero order of activation function and joint weight, regulates only the number of weight and their value. As a result, this network only produces a fixed order model or control level. These obstacles, which affect preceeding architectures, have been performing finite ability to adapt uncertainty character of real world plant, such as driving dynamics and its desired control performance.

This paper introduces a new concept of neural network neuron. In this matter, exploiting discrete z-function builds new neuron activation. Instead of zero order joint weight matrices, the discrete z-function weight matrix will be provided to realize uncertainty or undetermined real word plant and desired adaptive control system that their order has probably been changing. Instead of using bias, an initial condition value is developed. Neural networks using new neurons is called Varied Order Neural Network (VONN). For optimization process, updating order, coefficient and initial value of node activation function uses GA; while updating joint weight, it applies both back propagation (combined LSE-gauss Newton) and NPSO. To estimate the number of hidden layer, constructive back propagation (CBP) was also applied.

Thorough simulation was conducted to compare the control performance between FONN and MONN. In order to control, vehicle stability was equipped by electronics stability program (ESP), electronics four wheel steering (4-EWS), and active suspension (AS). 2000, 4000, 6000, 8000 data that are from TODS, a hidden layer, 3 input nodes, 3 output nodes were provided to train and test the network of both the uncertainty model and its adaptive control system.

The result of simulation, therefore, shows that stability parameter such as yaw rate error, vehicle side slip error, and rolling angle error produces better performance control in the form of smaller performance index using FDNN than those using MONN.

A solution to channel assignment problem in cellular networks is self-organizing channel assignment algorithm with distributed control. In this paper, we propose three cellular learning automata based dynamic channel assignment algorithms. In the first two algorithms, no information about the status of channels in the whole network will be used by cells for channel assignment whereas in the third algorithm, the additional information regarding status of channels may be gathered and then used by cells in order to allocate channels. The simulation results show that by using the proposed channel assignment algorithms the micro-cellular network can self-organize itself. The simulation results also show that the additional information used by the third algorithm help the cellular learning automata to find an assignment which results in lower blocking probability of calls for the network.

In this paper, we proposed hybrid-KNN methods on the Arabic script web page language identification. One of the crucial tasks in the text-based language identification that utilizes the same script is how to produce reliable features and how to deal with the huge number of languages in the world. Specifically, it has involved the issue of feature representation, feature selection, identification performance, retrieval performance, and noise tolerance performance. Therefore, there are a number of methods that have been evaluated in this work; k-nearest neighbor (KNN), support vector machine (SVM), backpropagation neural networks (BPNN), hybrid KNN-SVM, and KNN-BPNN, in order to justify the capability of the state-of-the-art methods. KNN is prominent in data clustering or data filtering, SVM and BPNN are well known in supervised classification, and we have proposed hybrid-KNN for noise removal on web page language identification. We have used the standard measurements which are accuracy, precision, recall and F1 measurements to evaluate the effectiveness of the proposed hybrid-KNN. From the experiment, we have observed that BPNN is able to produce precise identification if the data set given is clean. However, when increasing the level of noise in the training data, KNN-SVM performs better than KNN-BPNN against the misclassification data, even on the level of 50% noise. Therefore, it is proven that KNN-SVM produce promising identification performance, in which KNN is able to reduce the noise in the data set and SVM is reliable in the language identification.

A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.