Remarks on robot controller application of Clifford multi-layer neural networks

Abstract

In this paper, Clifford multi-layer neural networks using back-propagation algorithm are applied to inverse kinematics control of a robot manipulator as a first step of utilizing Clifford neural networks for robot control applications. The control system based on the on-line specialized learning architectures is considered and its characteristics are investigated. To increase the success rate of learning in this architecture, the weight-resetting methods are introduced into the drawback learning of the Clifford multi-layer neural network. In the computational experiments, the training of Clifford neural networks converges with a fewer number of iterations compared with the real number neural network which has more complex network topology and more parameters. The Clifford algebra framework makes the neural computing more efficient in inverse kinematics controller which shows the potential of the Clifford multi-layer neural network in robot control system.

Synchronization in Finite-Time Analysis of Clifford-Valued Neural Networks with Finite-Time Distributed Delays

“…In summary, the conditions in (26) hold. Referring to Definition (2), the NN models ( 7) and ( 8) are synchronize with the NN models ( 9) and (10) in finite time under controller (13).…”

“…Clifford algebra provides a solid principle to solve geometry problems. It has been implemented in many areas, such as neural computing [20][21][22][23][24], and computer and robot vision [25][26][27]. Clifford-valued NN models present a generalization of real-, complex-, and quaternion-valued NN models.…”

Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

“…2.1.1 Multi-layer feed-forward neural networks. Multi-layer NNs (MLP-NNs) are one of the most common and popular feed-forward neural architectures (Cui et al, 2014). This model has been established of one input layer, one or more hidden layers and one output layer.…”