Initial Input Vector Research Articles

Direct sequence estimation: a functional network approach

Functional networks (FNs) have shown excellent performance in probability, statistics, engineering applications, etc., but so far no methods of direct sequence estimation (DSE) for communication systems using FN have been published. The paper presents a new DSE approach using FN, which can be applied to cases with plural source signal sequence, short sequence or even the absence of training sequence. The proposed method can estimate the source sequence directly from the observed output data without training sequence and pre-estimating the channel impulse response. Firstly, a multiple-input multiple-output FN (MIMOFN), in which the initial input vector is devised via QR decomposition of receiving signal matrix, is adopted to solve the special issue. Meantime, a design method of the neural function for this special MIMOFN is proposed. Then, the learning rule for the parameters of neural functions is trained and updated by back-propagation learning algorithm. Finally, a simulation experiment is performed, the feasibility and accuracy of the method are showed from the experimental results, and some special simulation phenomena of the algorithm are observed.

Spectral hypergraph theory of the adjacency hypermatrix and matroids

We define a matroid from an m-uniform hypergraph. Spectral properties of the adjacency hypermatrix which can be deduced from the matroid are investigated. In particular, relationships between the coordinates of eigenvectors are revealed in terms of circuits of the matroid. It is expected that these relationships will lead to a better initial input vector on the interior of the positive cone for numerical calculations of Z-eigenvalues using the SS-HOPM method and for H-eigenvalues when using the NQZ method.

Neural Network Based Identification of r-TPA Production and Glycosylation in CHO Cells

An artificial neural network (ANN) modeling scheme was constructed for the identification of both recombinant tissue-type plasminogen activator (r-tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed-forward backpropagation neural networks were constructed to function as a software sensor over a wide range of shear-induced culture states. This enabled predictions of ammonia production, viable cell density, r-tPA production and glycosylation. The sensor was based on an initial input vector space consisting of simple metabolite concentrations, batch cultivation time, and a description of shear stress applied to the culture.

On the characteristics of the quadratic order associative memory that uses synchronous update and direct convergence.

A statistical method is applied to explore the characteristics of a certain class of quadratic order associative memories: the Synchronous Update Direct Convergence Memory (SQDM). The initial input vector is required to converge, with a given value of probability Pdc, to a stored codeword in just one synchronous update. The memory capacity ms is derived by a figure of merit N phi, and its tight asymptotic bound is found by using Tchebycheff's inequality. When the input contains erroneous bits, the maximum allowable number of attractors and their attraction radii are determined. The existence of principal connections Tpr useful in solving the problem of proliferation of connections in the SQDM is introduced. We proved that Tpr is a set of which an arbitrary connection Tijk satisfies square root of ms < or = [Tijk[ < or = 2 square root of ms.

On Optimal Consumption Plans in a Multi-sector Economy

We consider a stationary, productive technology which is described mathematically by a closed convex cone (see (T. 1) and (T. 3)). Labour appears in an explicit form as a factor of production. Free disposal of labour and commodity inputs is assumed (see T. 2)). Storage activities may also exist (see (T. 4)). In defining feasible plans we assume, besides the usual technological restrictions (see (F. 2), (F. 3), and (F. 4)) that population and labour force grow at a geometrical rate which is independent of time and of the level of economic activity (see (L)). Our criterion for optimality of consumption is obtained by discounting a stationary, concave, increasing and continuous utility of per capita consumption (see (U. 1), (U. 2), (4.1) and (7.0)). Section 2 contains some useful definitions and notation. The model is described in Section 3. In Section 4 we reformulate the model in per capita terms in order to simplify our notation and facilitate mathematical investigation of the model. The basic properties of the set of all feasible consumption plans starting from a given initial input vector are established in Section 5. Section 6 deals with stationary consumption plans. We show that, starting with a strictly positive input vector, we can approach any stationary consumption plan by a feasible one (see Corollary 6.3). In Section 7 we show that the class of all feasible plans starting from the same initial input vector contains an optimal plan (see Theorem 7.1). The last section deals with the problem of characterizing optimal plans, and is both of theoretical and computational significance. Sufficient and necessary conditions for optimality of a plan in the class of all plans starting from a given strictly positive initial input vector are given. Our conditions are the three classical ones: the gradient condition (see (8.15)), the intertemporal profit maximization principle (see (8.16)), and the vanishing in the limit of the present value of input (see (8.17)).