International Journal of Computational Intelligence and Applications

This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey–Glass model and identification of two nonlinear dynamic systems reveal that the performance of proposed algorithm is much better and it shows quite satisfactory results.

In this paper, we present an approach for predicting algae growth through the selection of influential environmental variables. Chlorophyll a is considered to be an indicator for algal biomass and we predict this as a proxy for algae growth. Environmental variables like water temperature, salinity, etc. have influence upon algae growth. Depending on the geographic location, the influence of these environmental variables will vary. Given a set of relevant environmental variables we perform feature selection using a number of algorithms to identify the variables relevant to the growth. We have developed an influence matrix-based approach to select the relevant features. The selected features are then used for predicting algae growth using different regression algorithms to identify their relative strength. The approach is tested on the algae data of Derwent estuary in Tasmania. The experimental results demonstrate that the accuracy of algae growth prediction with influence matrix-based feature selection is superior to using all the features.

This paper introduces a novel identifier scheme for identification of nonlinear systems with disturbances. The identification process is carried out in two steps: an offline procedure and an online procedure. The method comprises of an automatic structure generating phase using entropybased technique. The accuracy of the model is suitably controlled using the entropy measure. The parameter learning phase uses the backpropagation technique. To improve the accuracy and also for generalization of the model to handle different data sets, Differential Evolution technique is employed whereby the parameters of the model are suitably tuned using evolutionary technique. A semi serial-parallel model is introduced to improve the online identification process in the presence of noisy data. The proposed mechanism is utilized and compared against the classical Sugeno, adaptive network-based fuzzy inference system (ANFIS) modeling and Laguerre Network-Based Fuzzy System for the identification of a nonlinear benchmark problem. In addition, the proposed technique is also used to model a rotary wing unmanned aerial vehicle (UAV) from real test input–output data. The modeling performance and generalization capability are seen to be superior with our method.

This paper describe a new concept of "cluster outlier-ness". In order to quantify it, we propose a relative isolation score named group outlier factor (GOF). GOF is a score, which is computed during a clustering process using self-organizing maps. The main difference between GOF and existing methods is that, being an outlier is not associated to a single pattern but to a cluster. Thus, an outlier factor (OF) with respect to each cluster is computed for each new sample and compared to the GOF score associated for each cluster. OF is used as a novelty detection classifier. This approach allows to identify meaningful outlier-clusters and detects novel patterns that previous approaches could not find. Experimental results and comparison studies show that the use of GOF sensibly improves the results in term of cluster-outlier and novelty detection.

This paper presents the problem of decision making by a judge in the case of murder cases in criminal law using single hidden layered fuzzy neural network algorithm. Since the membership functions (MFs) of fuzzy sets can affect the performance of the classification models, determination of MFs is crucial. In this paper, the MF selected is Triangular and Gaussian is proposed for evaluation to improve the classification results. To evaluate the effectiveness of the proposed Fuzzy Neural Network model for the classification of murder cases, sufficient number of real-world data sets of court decisions are trained and tested. The simulation model of different membership functions for the modified fuzzy neural network architecture is implemented in C++. Experimental results show that the proposed neuro-fuzzy classifier with Gaussian MF outperforms Triangular MF with higher accuracy. The proposed classification model is proved to be a suitable tool for classification of murder cases in criminal law.

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.