Piecewise w∞-equitable efficiency in multiobjective programming

Acta Mathematicae Applicatae Sinica, English Series - In equitable multiobjective optimization all the objectives are uniformly optimized, but in some cases the decision maker believes that some of...     

Predicting cortical oscillations with bidirectional LSTM network: a simulation study

It has been stated that up-down-state (UDS) cortical oscillation levels between excitatory and inhibitory neurons play a fundamental role in brain network construction. Predicting the time series behaviors of neurons in periodic and chaotic regimes can help in improving diseases, higher-order human activities, and memory consolidation. Predicting the time series is usually done by machine learning methods. In paper, the deep bidirectional long short-term memory (DBLSTM) network is employed to predict the time evolution of regular, large-scale UDS oscillations produced by a previously developed neocortical network model. In noisy time-series prediction tasks, we compared the DBLSTM performance with two other variants of deep LSTM networks: standard LSTM, LSTM projected, and gated recurrent unit (GRU) cells. We also applied the classic seasonal autoregressive integrated moving average (SARIMA) time-series prediction method as an additional baseline. The results are justified through qualitative resemblance between the bifurcation diagrams of the actual and predicted outputs and quantitative error analyses of the network performance. The results of extensive simulations showed that the DBLSTM network provides accurate short and long-term predictions in both periodic and chaotic behavioral regimes and offers robust solutions in the presence of the corruption process.

     

Complete dynamical analysis of a neocortical network model

Nonlinear Dynamics - The brain is a complex system consisting of a large number of interacting neurons. Recently, a simple nonlinear biological model has been proposed for the up and down state...     

Piecewise equitable efficiency in multiobjective programming

In equitable multiobjective optimization all the objectives are uniformly optimized, but in some cases the decision maker believes that some of them should be uniformly optimized. To solve this problem in this paper, the original problem is decomposed into a collection of smaller subproblems, according to the decision maker, and then the subproblems are solved by the concept of equitable efficiency. Furthermore, by using the concept of P$P$-equitable efficiency two models are presented to coordinate equitably efficient solutions of subproblems.     

Some inequalities involving upper bounds for some matrix operators I

In this paper we consider the problem of finding upper bounds of certain matrix operators such as Hausdorff, Nörlund matrix, weighted mean and summability on sequence spaces l _p(w) and Lorentz sequence spaces d(w, p), which was recently considered in [9] and [10] and similarly to [14] by Josip Pecaric, Ivan Peric and Rajko Roki. Also, this study is an extension of some works by G. Bennett on l _p spaces, see [1] and [2].     

Inequalities involving upper bounds for certain matrix operators

In this paper, we considered the problem of finding the upper bound Hausdorff matrix operator from sequence spaces l_p(v) (ord(v, p)) intol _p (w) (ord(w, p)). Also we considered the upper bound problem for matrix operators fromd(v, 1) intod(w, 1), and matrix operators frome(w, ∞) intoe(v, ∞), and deduce upper bound for Cesaro, Copson and Hilbert matrix operators, which are recently considered in [5] and [6] and similar to that in [10].     

Lower bounds for matrices on block weighted sequence spaces I

In this paper we consider some matrix operators on block weighted sequence spaces l _p (w, F). The problem is to find the lower bound of some matrix operators such as Hausdorff and Hilbert matrices on l _p (w, F). This study is an extension of papers by G. Bennett, G.J.O. Jameson and R. Lashkaripour.