anti-Hebbian Algorithm Research Articles

Learning-Based Anti-Hebbian Algorithm for Control of Distribution Static Compensator

This paper deals with an implementation of a distribution static compensator (DSTATCOM) using a learning-based anti-Hebbian control algorithm for compensation of linear/nonlinear loads. The proposed control algorithm is used for extraction of fundamental active and reactive power components of load currents for estimating reference supply currents. This control algorithm is implemented on a developed DSTATCOM using a digital signal processor for reactive power compensation, harmonics elimination, and load balancing. Simulation and test results demonstrate satisfactory performance of the proposed control algorithm for the control of DSTATCOM under varying loads.

Functional changes induced by multiple plasticity rules in the hippocampal circuit: experiment and theory

We attempt to maximise mutual information in a neural network where the output signal is corrupted by noise in the transmission process to the next stage. We assume that the average power available for transmission is limited and that the transmission noise is uncorrelated equal-variance Gaussian noise. Using the Lagrange multiplier technique, we construct a utility function that should be maximised to achieve our optimum. This optimum will be achieved when the output signal components are also uncorrelated and of equal variance. For a linear network with lateral inhibitory connections, using an anti-Hebbian algorithm with modified self-inhibitory connections we guarantee to increase our utility function over time and converge to the required optimum, although not by steepest ascent. For a network with inhibitory interneurons, a combined Hebbian/anti-Hebbian algorithm will similarly increase the utility function over time to find the optimum. This network with interneurons suggests that negative feedback may be used in a perceptual system to optimise forward transmission of information.

Efficient information transfer and anti-Hebbian neural networks

We attempt to maximise mutual information in a neural network where the output signal is corrupted by noise in the transmission process to the next stage. We assume that the average power available for transmission is limited and that the transmission noise is uncorrelated equal-variance Gaussian noise. Using the Lagrange multiplier technique, we construct a utility function that should be maximised to achieve our optimum. This optimum will be achieved when the output signal components are also uncorrelated and of equal variance. For a linear network with lateral inhibitory connections, using an anti-Hebbian algorithm with modified self-inhibitory connections we guarantee to increase our utility function over time and converge to the required optimum, although not by steepest ascent. For a network with inhibitory interneurons, a combined Hebbian/anti-Hebbian algorithm will similarly increase the utility function over time to find the optimum. This network with interneurons suggests that negative feedback may be used in a perceptual system to optimise forward transmission of information.

Learning algorithm for total least-squares adaptive signal processing

A new adaptive learning algorithm, called the constrained anti-Hebbian algorithm, is presented. This algorithm is optimal in the total least-squares sense, simple to use, and can be applied to on-line adaptive FIR and IIR filtering directly. Simulation results confirm that the proposed algorithm provides significantly better performance over the least mean-squares (LMS) and recursive least-squares (RLS) algorithms.