TMLNN: triple-valued or multiple-valued logic neural network

Granular computing with multiple granular layers for brain big data processing

Big data is the term for a collection of datasets so huge and complex that it becomes difficult to be processed using on-hand theoretical models and technique tools. Brain big data is one of the most typical, important big data collected using powerful equipments of functional magnetic resonance imaging, multichannel electroencephalography, magnetoencephalography, Positron emission tomography, near infrared spectroscopic imaging, as well as other various devices. Granular computing with multiple granular layers, referred to as multi-granular computing (MGrC) for short hereafter, is an emerging computing paradigm of information processing, which simulates the multi-granular intelligent thinking model of human brain. It concerns the processing of complex information entities called information granules, which arise in the process of data abstraction and derivation of information and even knowledge from data. This paper analyzes three basic mechanisms of MGrC, namely granularity optimization, granularity conversion, and multi-granularity joint computation, and discusses the potential of introducing MGrC into intelligent processing of brain big data.

A NEW UNIFORM NEURON MODEL OF GENERALIZED LOGIC OPERATORS BASED ON [a,b]

The study on neural systems is very hot, especially regarding modelling of fuzzy neural networks. The neuron models have been limited in interval [0,1] before. This paper studies the logic operators and neuron models of proposition object based on [a, b]. Any interval [a, b] is called a generalized interval. Firstly, authors provide the conception of proposition object, discussed on the radix space of universal logic. Secondly, using the NTS norms theories, this paper establishes the universal logic operation models on generalized interval [a, b]. Thirdly, the paper builds up a new uniform neuron model of "Not/And/Or/Average" on generalized interval [a, b]. As an instance, authors discuss the neuron models based on standard interval [0,1], which are continuously changeable with generalized correlation coefficient "h" and generalized self-correlation coefficient "k". This work offers important theories and models for neurons reasoning, make it more flexible because of the change of h, k and [a, b], and enlarge its study domain.

STRIP--a strip-based neural-network growth algorithm for learning multiple-valued functions

We consider the problem of synthesizing multiple-valued logic functions by neural networks. A genetic algorithm (GA) which finds the longest strip in V is a subset of K(n) is described. A strip contains points located between two parallel hyperplanes. Repeated application of GA partitions the space V into certain number of strips, each of them corresponding to a hidden unit. We construct two neural networks based on these hidden units and show that they correctly compute the given but arbitrary multiple-valued function. Preliminary experimental results are presented and discussed.