Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices

Brain emotional learning impedance control of uncertain nonlinear systems with time delay: Experiments on a hybrid elastic joint robot in telesurgery

Telesurgical robot control is a significant example of an uncertain nonlinear system, as it involves various complexities, including unknown master/slave dynamics, environmental uncertainties, joint elasticities, and communication time delays. This problem becomes even more complicated when desirable properties such as stability, transparency, rigidity, accuracy, and fine manipulability are considered. We consider an elastic joint telesurgical robot architecture that combines two parallel and serial manipulators to achieve the desired rigidity, accuracy, and fine manipulability. For this purpose, we propose using Brain Emotional Learning (BEL) to estimate the robot's uncertain nonlinear dynamics. In contrast to recent stability analyses of BEL-based systems, we employ Lyapunov theory to achieve the closed-loop system's general stability independent of robot dynamics and chattering. Furthermore, the proposed control architecture implements two reference impedance models for the master and slave robots' trajectory generation and makes a trade-off between transparency and stability by simultaneously considering optimal position synchronization and transparency conditions. In this regard, we extend these two conditions in absolute stability theory and Llewellyn's criterion to obtain the allowable bound of communication time delay. The proposed robot is designed and experimentally implemented at the Robotics Laboratories at FUM and SUT Universities. Along with confirming the theoretical results, simulations and laboratory experiments demonstrate that a reasonable trade-off between stability and transparency is made in four realistic case studies with and without communication time delays.

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks

Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods.

A novel single neuron perceptron with universal approximation and XOR computation properties

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.