A Novel Multivalued Associative Memory System

Globally coupled map (GCM) model can evolve through chaotic searching into several stable periodic orbits under properly controlled parameters. This can be exploited in information processing such as associative memory and optimization. In this paper, we propose a novel covariance learning rule for multivalue patterns and apply it in memorization of gray scale images based on modified GCM model (S GCM). Analysis of retrieval results are given finally.

BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE

The multiple classifier system （MCS）, composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory （QLBAM） to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate ＂overproduce and choose＂ strategy or ＂thinning＂ algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability.

DISCRETE BIDIRECTIONAL ASSOCIATIVE MEMORY WITH LEARNING FUNCTION

In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.

Memory and Aesthetics： Study of Musical Quotations in Ives’s and Crumb’s Music

Throughout Western music history, pre-existing material has long been the aesthetic core of a new composition. Yet there has never been such an epoch as our time in which using pre-existing material, melodic quotation in particular, features so extensively in works of many of the composers. The aim of this paper is to investigate how the use of quoted tunes in a musical piece operates in an interwoven complex where time and space are of the essence. A quote is able to oscillate perpetually between one’s mental worlds of the memorable past and the imaginative present when it is highlighted enough to be recognizable from its surrounding context. Upon interpreting the use of quotation in various contexts, the aesthetic object, I argue, is the shift from original to quoted music, and vice versa. And listeners can respond aesthetically to the quotation itself even without knowledge of its provenance and textual or referential content.

A novel memristive neural network with hidden attractors and its circuitry implementation

Neural networks have been applied in various fields from signal processing, pattern recognition, associative memory to artifi- cial intelligence. Recently, nanoscale memristor has renewed interest in experimental realization of neural network. A neural network with a memristive synaptic weight is studied in this work. Dynamical properties of the proposed neural network are investigated through phase portraits, Poincar6 map, and Lyapunov exponents. Interestingly, the memristive neural network can generate hyperchaotic attractors without the presence of equilibrium points. Moreover, circuital implementation of such memristive neural network is presented to show its feasibility.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

This paper is concerned with fractional-order bidirectional associative memory(BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

Creative Nonfiction： A Three-Quarter Profile

Creative nonfiction is coming of age, not simply as opposed to ficdon, but as respectable as good ficdon true to its name. Due attention should be drawn to this super-genre absorbing varied subject matter. This paper looks into creative nonfiction as a form of art with a let-its-hair-down attitude generally in line with the genre. Creative nonfiction is held to be a paradoxically generic term covering subcategories such as memoir, biography, autobiography, history, and journalism. Instead of being walled off from one another, these subcategories mix and match to build up a work of truth, or rather elusive truth, which might sound stranger than fiction. In addition to the imaginative mix and match to open up new possibilities for writing, felicity of style is another add-on to doing it well. Hence creative nonfiction is conceived as an adventure to wrap up a somewhat strange story in a serendipitous form as well as a felicitous language. Hopefully, this three- quarter profile of the genre will be able to offer a thin slice of the picture to the audience.