Catálogo de publicaciones - libros

A novel multi-input, multi-output generalized radial basis function (RBF) neural networks for nonlinear system modelling is presented in the paper, which uses extend Kalman filter to sequentially update both the output weights and the centers of the network. Simultaneously, such RBF models employ radial basis functions whose form is determined by admissible exponential generator functions. To test the validity of the proposed method, this paper demonstrates that generalized RBF neural networks with the extended Kalman filter can be used effectively for the identification and modelling of nonlinear dynamical systems. Simulation results reveal that the new generalized RBF networks guarantee faster learning and very satisfactory function approximation capability in modeling nonlinear dynamic systems.

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.

Path planning is an important task in mobile robot control. When the robot must move rapidly from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. In this paper, an obstacle avoidance path planning approach for mobile robots is proposed by using Ant-Q algorithm. Ant-Q is an algorithm in the family of ant colony based methods that are distributed algorithms for combinatorial optimization problems based on the metaphor of ant colonies. In the simulation, we experimentally investigate the sensitivity of the Ant-Q algorithm to its three methods of delayed reinforcement updating and we compare it with the results obtained by other heuristic approaches based on genetic algorithm or traditional ant colony system. At last, we will show very good results obtained by applying Ant-Q to bigger problem: Ant-Q find very good path at higher convergence rate.

This paper proposes an algorithm to detect the obstacles in outdoor unstructured environment with monocular vision. It makes use of motion cues in the video streams. Firstly, optical flow at feature points is calculated. Then rotation of the camera and FOE(focal of expansion) are evaluated separately. A non-linear optimization method is adopted to refine the rotation and FOE. Finally, we get inverse TTC(time to contact) with rotation and FOE and detect the obstacles in the scene. The algorithm doesn’t need any assumption that the ground is flat or partially flat as the conventional methods. So it is suitable for outdoor unstructured environment. Qualitative and quantitative experiment results show that our algorithm works well on different kinds of terrains.

This paper proposes a novel attention selection system with competition neural network supervised by visual memory. As compared with others, this system can not only attend some salient regions randomly according to sensory information but also mainly focus on some learned objects by the visual memory. So it can be applied in robot self-localization or object tracking. The weights of neural networks can be adapted in real time to environment change.

A robust adaptive control based on generalized dynamic fuzzy neural network (GD-FNN) is presented for robot manipulators. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. Being use of radial basis function neural network (RBFNN) the learning speed is very fast. The asymptotic stability of the control system is established using Lyapunov theorem. Simulations are given for a two-link robot in the end of paper, and validated the control arithmetic.

Due to the time-consuming calculation for the forward kinematics of a 3-PRS (prismatic-revolute-spherical) parallel manipulator, neither the kinematic nor dynamic control algorithm can be implemented on real time. To deal with such problem, the forward kinematics is solved by means of artificial neural network (NN) approach in this paper. Based on the trained NN, the kinematic control of the manipulator is carried out by resorting to an ordinary control algorithm. Simulation results illustrate that the NN can approximate the forward kinematics perfectly, which leads to ideal control results of the parallel manipulator.

Multi-Constrained Shortest Path (MCSP) selection is a fundamental problem in communication networks. Since the MCSP problem is NP-hard, there have been many efforts to develop efficient approximation algorithms and heuristics. In this paper, a new algorithm is proposed based on vectorial Autowave-Competed Neural Network which has the characteristics of parallelism and simplicity. A nonlinear cost function is defined to measure the autowaves (, paths). The -paths limited scheme, which allows no more than autowaves can survive each time in each neuron, is adopted to reduce the computational and space complexity. And the proportional selection scheme is also adopted so that the discarded autowaves can revive with certain probability with respect to their cost functions. Those treatments ensure in theory that the proposed algorithm can find an approximate optimal path subject to multiple constraints with arbitrary accuracy in polynomial-time. Comparing experiment results showed the efficiency of the proposed algorithm.

This paper presents a neuro intelligent virtual leader based approach for close formation of a group of mobile vehicles. Neural Network-based trajectory planning is incorporated into the leading vehicle so that an optimal reference path is generated automatically by the virtual leader, which guides the whole team vehicles to the area of interest as precisely as possible. The steering control scheme is derived based on the structural properties of the vehicle dynamics. Simulation on multiple vehicles formation is conducted as a verification of the effectiveness of the proposed method.

This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks’ problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories’ time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes.