For this purpose, a new soft computing approach namely, least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing optimization technique is implemented.
A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was proposed (CSA-LSSVM model).
A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization tool was proposed.
To verify the effectiveness of the established T S FRNN model for the pH neutralization process, it is compared with two T S FRNN models optimized with other methods.
ROC indicates the performance of all SVM models optimized with best parameters.
This is fact is well exemplified by the classification task undertaken in our study in which we found that the models optimized with linear kernel functions are less accurate as compared to those trained with non-linear functions.
The optimization model is presented with the distortion as the optimization objective, and the model is optimized with an upper limit, a lower limit and the constraint function by the non-linear method and the Lagrange multiplier method.
In this research, the LSSVM model is optimized with a Coupled Simulated Annealing (CSA) optimization algorithm.
Subsequently, each model is optimized with the reduced parameter set, using the linear regression model for the non-optimized parameters.
The model is optimized with Rosenbrock's method.
