A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this netwo...

I propose an axiomatic framework for belief revision when new information is qualitative, of the form “event A at least as likely event B .” My decision maker need not have beliefs about joint distribution signal she will receive and payoff‐relevant states. three axioms, Exchangeability , Stationarity Reduction to characterize class pseudo‐Bayesian updating rules. The key axiom, requires that o...

A blind algorithm with implicit signal selectivity capability is proposed. The algorithm is an evolution of the original multiuser constant modulus algorithm of [1]. The new algorithm features a least-squares type updating rule for fast convergence rate and an adaptive control of the weight of the decorrelation term which improves the steady-state error variance. The expected improvements of th...

This paper talks about the problems in particle swarm optimization (PSO), including local optimum and difficulty in improving solution accuracy by fine tuning. We presents a new variation of Adaptive Tribe-PSO model where nonlinear updating of inertia weight and a particle’s fitness with TribePSO model are combined to improve the speed of convergence as well as fine tune the search in the multi...