COMPUTATIONAL INTELLIGENCE approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models proposed in several past decades, it is widely recognized that exchange rates are extremely difficult to forecast. Artificial Neural Networks (ANNs) are one of the most accurate and widely used forecasting models that have been successfully applied for exchange rate forecasting. In this paper, a hybrid model is proposed based on the basic concepts of artificial neural networks in order to yield more accurate results than the traditional ANNs in short span of time situations. Three exchange rate data sets—the British pound, the United States dollar, and the Euro against the Iran rial-are used in order to demonstrate the appropriateness and effectiveness of the proposed model. Empirical results of exchange rate forecasting indicate that hybrid modelis generally better than artificial neural networks and other models presented for exchange rate forecasting, in cases where inadequate historical data are available. Therefore, our proposed model can be a suitablealternative model for financial markets to achieve greater forecasting accuracy, especiallyin incomplete data situations.

COMPUTATIONAL INTELLIGENCE techniques have a great potential to solve different COMPUTATIONAL problems in engineering sciences. In this paper, modeling and simulation of down hole drilling motor using the COMPUTATIONAL INTELLIGENCE methods such as artificial neural network (ANN), radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) is presented. Experimental data are used to train and test the proposed models. The results of the proposed models are compared with the experimental data. The predicated values are found to be in a good agreement with the experimental values. Also, they are very faster than the experimental measurement method. These compact models can reduce the COMPUTATIONAL time while keeping the accuracy of physics-based model and allow the fast and accurate system level simulation and modeling of industrial packages.Finally, using the proposed ANN model, which is the best proposed model, an equation to describe the nonlinear behavior of down hole drilling motor is introduced.

This study investigates the oil extraction from Pistacia Khinjuk by the application of enzyme. Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were applied for modeling and prediction of oil extraction yield. 16 data points were collected and the ANN was trained with one hidden layer using various numbers of neurons. A two-layered ANN provides the best results, using application of ten neurons in the hidden layer. Moreover, process optimization were carried out by using both methods to predict the best operating conditions which resulted in the maximum extraction yield of the Pistacia Khinjuk. The maximum extraction yield of Pistacia Khinjuk was estimated by ANN method to be 56.52% under the operational conditions of temperature and enzyme concentration of 0.27, pH of 6, and the Ultrasonic time of 4.23 h, while the optimum oil extraction yield by ANFIS method was 55.8% by applying the operational circumstances of enzyme concentration of 0.30, pH of 6.5, and the Ultrasonic time of 4.55 h. In addition, mean-squared-error (MSE) and relative error methods were utilized to compare the predicted values of the oil extraction yield obtained for both models with the experimental data. The results of the comparisons revealed the superiority of ANN model as compared to ANFIS model.

Compared to drag anchors, suction caissons (Q) in clays often provide a cost-effective alternative for jacket structures, catenary, tension leg moorings, and taut leg. In this research, two COMPUTATIONAL approaches are proposed for predicting the uplift capacity of Q in clays. The proposed approaches are based on the combinations of adaptive network-based fuzzy inference system (ANFIS) models (ANFIS-subtractive clustering (ANFIS-SC) and ANFIS-fuzzy c-means (ANFIS-FC)) with metaheuristic techniques (ant colony optimization (ACO) or particle swarm optimization (PSO)). In these approaches, the PSO and ACO algorithms are employed to enhance the accuracy of ANFIS models. In order to develop hybrid models, a comprehensive database from open-source literature is used to train and test the proposed models. In these models, d (diameter of caisson), L (embedded length), D (depth), Su (undrained shear strength of soil), θ (inclined angle), and Tk (load rate parameter) were used as the input parameters. The performance of all models was evaluated by comparing performance indexes, i. e., means squared error and squared correlation coefficient. As a result, PSO and ACO can be used as reliable algorithms to enhance the accuracy of ANFIS models. Moreover, it was found that the ANFIS– subtractive clustering-ACO model provides better results in comparison with other developed hybrid models.