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Echo State Networks, ESNs, are standardly composed of additive units undergoing sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure called reservoir. Coming up with a good reservoir depends mainly on picking up the right parameters for the network initialization. Human expertise as well as repeatedly tests may sometimes provide acceptable parameters. Nevertheless,...
AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum...
Echo State Networks ESNs are specific kind of recurrent networks providing a black box modeling of dynamic non-linear problems. Their architecture is distinguished by a randomly recurrent hidden infra-structure called dynamic reservoir. Coming up with an efficient reservoir structure depends mainly on selecting the right parameters including the number of neurons and connectivity rate within it. Despite...
This paper deals with the Job-shop scheduling problem. We propose to solve this problem by exploiting the Particle Swarm Optimization Global Velocity (PSOVG) algorithm. The PSOVG by its nature focus on the global optimum within a given set of solutions. In this paper a solution is PSO particle, it consists in a possible scheduling solution for the given problem. The PSO-VG-JSSP is a PSO-VGO with a...
Echo state networks (ESNs) fulfill considerable promises for topology fine-tuning in supervised training. However the randomness of the setting of ESN weights initialization affects badly the learning performance. On the other side, Particle Swarm Optimization (PSO) has proven its efficiency as an optimization tool to puzzle out optimal solutions in complex space. In this work, we present an ESN architecture...
Bio-inspired techniques and swarm intelligence are used to solve complex problems. In this paper, two new variants of AS-PSO (Ant Supervised by Particle Swarm optimization) meta-heuristic are proposed and applied to a classical travelling salesman benchmark problem. The new variants are Fuzzy-AS-PSO and Simplified AS-PSO (S-AS-PSO). AS-PSO is a hierarchical meta-heuristic based on the ant colony optimisation...
In this paper the investigation is placed on the hierarchic neuro-fuzzy systems as a possible solution for biped control. An hierarchic controller for biped is presented, it includes several sub-controllers and the whole structure is generated using the adaptive Neuro-fuzzy method. The proposed hierarchic system focus on the key role that the centre of mass position plays in biped robotics, the system...
This paper investigates the relative performances of PSO variants when used to solve inverse kinematics. Inverse kinematics is a key issue in robotics; for problems such as path planning, motion generation or trajectories optimization, they are classically involved. In the specific case of articulated robotics, inverse kinematics is needed to generate the joint motions, correspondent to a known target...
This paper reports an experience of use of a robotics educational kit to prototype a biped robot, the robot was used to validate a new walking trajectory generation approach. The use of robotic kits is an alternative for low-cost prototyping and testing robotic solutions. Even if these robots did not meet the requirements of an industrial design, they can offer a first validation alternative. In this...
In this paper we propose a method to generate gaits of a biped robot by a particle swarm optimization algorithm. The system generates angular positions for joints with an interpolate end segments positions to evaluate walking stability. The proposed PSO is adapted to generate angular position joints, Human walking stability criteria are used to check and validate the gaits. The experimental procedure...
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