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—This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples(More)
We study phase synchronization in a network motif with a starlike structure in which the central node's (the hub's) frequency is strongly detuned against the other peripheral nodes. We find numerically and experimentally a regime of remote synchronization (RS), where the peripheral nodes form a phase synchronized cluster, while the hub remains free with its(More)
In this paper, dynamical systems made up of locally coupled nonlinear units are used to control the locomotion of bio-inspired robots and, in particular, a simulation of an insect-like hexapod robot. These controllers are inspired by the biological paradigm of central pattern generators and are responsible for generating a locomotion gait. A general(More)
—We introduce a new methodology and experimental implementations for real-time wave-based robot navigation in a complex, dynamically changing environment. The main idea behind the approach is to consider the robot arena as an excitable medium, in which moving objects—obstacles and the target—are represented by sites of autowave generation: the target(More)
We address the problem of how the survival of cooperation in a social system depends on the motion of the individuals. Specifically, we study a model in which prisoner's dilemma players are allowed to move in a two-dimensional plane. Our results show that cooperation can survive in such a system provided that both the temptation to defect and the velocity(More)
This paper presents an innovative wormlike robot controlled by cellular neural networks (CNNs) and made of an ionic polymer-metal composite (IPMC) self-actuated skeleton. The IPMC actuators, from which it is made of, are new materials that behave similarly to biological muscles. The idea that inspired the work is the possibility of using IPMCs to design(More)
In the paper a new structure of Multi-Layer Perceptron, able to deal with quaternion-valued signal, is proposed. A learning algorithm for the proposed Quaternion MLP (QMLP) is also derived. Such a neural network allows to interpolate functions of quaternion variable with a smaller number of connections with respect to the corresponding real valued MLP.(More)