Generation of Stable Trajectories for Lower Limb Exoskeletons using Neural Oscilla...
Theta - Fuzzy Associative Memory: Fundamentals, Extensions, and Applications
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Author(s): |
Marcelo Vieira
Total Authors: 1
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Document type: | Master's Dissertation |
Press: | São Carlos. |
Institution: | Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD) |
Defense date: | 1997-12-12 |
Examining board members: |
Aluizio Fausto Ribeiro Araújo;
Fernando Antonio Campos Gomide;
Álvaro Geraldo Badan Palhares
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Advisor: | Aluizio Fausto Ribeiro Araújo |
Abstract | |
The aim of this project is to develop an artificial neural networks model based on principles of associative memory. This neural network model must be able to solve the problem of trajectory planning and point to point control of a robot arm, which is located in a partially known and/or noisy environment. The proposed model is composed by two surfaces: the temporal sequence surface and the angle surface. For the temporal sequence surface the new propose model Temporal Multidirectional Associative Memmy (TMAM) is able to store and recall n-tuplas of information, to deal with noisy and/or incomplete information and to learn temporal sequences. TMAM uses a continuas representation and autoassociative feedback. A RBF model is used to implement the angle surface, which is liable for producing the angle information for the joint of the robot arm. The two surfaces compose the whole system which is liable for the trajectory planning and system control. Hence, the system receives information about the initial point and the target point, constructs the trajectory to reach the target point from the initial point and converts the spatial points which compose the trajectory, in values of joint angles. The obtained results show that TMAM model can recall, interpolate and extrapolate points in the sequences. The model has the ability of generating new trajectories and memorizing different size of sequences at the same time. This model also shows fast learning. The RBF model can recall the desired outputs with a small error and can receive a pattern which is formed by an unreachable final point and generate a set of angles which, in turn, represent a reachable final point. (AU) |