High precision position control and high speed control of the robot manipulators are fundamental and important control problems. The effectiveness of finite-time P-PI control was confirmed by end-effector position control of robot manipulators. However, parameter tuning method has not been proposed to finite-time P-PI control. In this paper, we propose a settling time design method and a parame...

S AbstractWe analyze the robust stability of Inverse LQ regulators for single-input linear systems with uncertain input delay. Unlike the usual LQ regulator, the Inverse LQ regulator has a gain tuning parameter that can be chosen freely to some extent without losing its LQ optimality. Utilizing this freedom we seek the range of tuning parameter that ensures the robust stability against the unce...

In this paper, we demonstrate the benefits of using a multi-objective approach when tuning the parameters of an Evolutionary Algorithm. To overcome the specific challenges that arise when using a meta-algorithm for parameter tuning on multiple functions, we introduce a new algorithm called the Multi-Function Evolutionary Tuning Algorithm (M-FETA) that is able to approximate the parameter Pareto...

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of ma...

This paper proposes a novel approach for directly tuning the gaussian kernel matrix for one class learning. The popular gaussian kernel includes a free parameter, σ, that requires tuning typically performed through validation. The value of this parameter impacts model performance significantly. This paper explores an automated method for tuning this kernel based upon a hill climbing optimizatio...

The further study on the sensitivity analysis of Neocognitron is discussed in this paper. Fukushima's Neocognitron is capable of recognizing distorted patterns as well as tolerating positional shift. Supervised learning of the Neocognitron is fulfilled by training patterns layer by layer. However, many parameters, such as selectivity and receptive fields are set manually. Furthermore, in Fukush...

Spark has been established as an attractive platform for big data analysis, since it manages to hide most of the complexities related to parallelism, fault tolerance and cluster setting from developers. However, this comes at the expense of having over 150 configurable parameters, the impact of which cannot be exhaustively examined due to the exponential amount of their combinations. The defaul...

Spark has been established as an attractive platform for big data analysis, since it manages to hide most of the complexities related to parallelism, fault tolerance and cluster setting from developers. However, this comes at the expense of having over 150 configurable parameters, the impact of which cannot be exhaustively examined due to the exponential amount of their combinations. The defaul...

We propose a statistical framework and a methodology for automatically characterizing the influence that a steering algorithm’s parameters have on its performance. Our approach uses three performance criteria: the success rate of an algorithm in solving representative scenarios, the quality of the simulations solution, and the algorithm’s computational efficiency. Given an objective defined as ...

This article introduces a generic sensitivity analysis method to measure the influence and interdependencies of Evolutionary Algorithms parameters. The proposed work focuses on its application to a Parallel Asynchronous Cellular Genetic Algorithm (PA-CGA). Experimental results on two different instances of a scheduling problem have demonstrated that some metaheuristic parameters values have lit...