Machine Learning is an area concerned with the automation of the process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge based systems use higher level knowledge representations. The method we propose here, provides a technique which automatically allows us to extract production rules from the lower level representation used by a single-layered neural networks trained by Hebb's rule. Even though a single-layered neural network can not model complex, nonlinear domains, their strength in dealing with noise has enabled us to produce correct rules in a noisy domain.

Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications

Whereas a significant amount of empirical work has been carried out in the area of logical neural networks, the fundamental theoretical basis is underdeveloped. This paper provides an analysis of recurrent nets of affine Boolean functions and defines the relation between their behaviour and that of linear Boolean nets. It is shown that the only effect on the unlabelled state structure of adding any number of inverters to a linear Boolean net is a restricted modification of cycle lengths.

The Behaviour of Affine Boolean Sequential Networks

In order to synthesize neural network structures with specific capabilities, a predictive theoretical model of the network configuration-behaviour relationship is needed. The problem is a very complex one and has been addressed by very few researchers. This paper considers just one fundamental logical feature, achievable cycle lengths, of very simple autonomous, synchronous, polyfunctional Boolean networks. The lack of a predictive theoretical model for the relationship between network configuration and cyclic behaviour is discussed, and some simulation results are reported. Several theorems and conjectures on the effect of connectivity restrictions on achievable cycle lengths are presented. The conjectures presented remain, thus far, unproven; the process of exploring possible methods for their proofs, or proofs of their unprovability, will provide insight into methods, and possibilities, of modelling many other aspects of network configuration-behaviour relationships.

Cyclic Behaviour of Autonomous, Synchronous Boolean Networks: Some Theorems and Conjectures

In this study, we estimated the impact of enhanced flow on shallow wetlands that receive major effluent discharge from an adjoining metropolitan city. The local people use the shallow wetlands for pisciculture. Beginning in 1998, the population of the city began to rise and the amount of effluent discharge increased. The excess load is now a problem for the city engineers, and they plan to manage the sewage by increasing the area of the sewage network. The depth of the wetlands is also decreasing due to an increase in suspended solids. The quantity of the toxic load suspended in the discharge has increased the silt content, which has consequently further reduced the depth of the wetlands. The excess flow on low depth wetlands may cause overflows and destroy both the ecosystem and the livelihood of the local people. In this study, the pattern identification capability of neuro-genetic models was used to estimate the impact of the excess flow on sewage-fed wetlands. Two neural network models were created to estimate whether fisheries can accommodate the enhanced flow. According to the results of this study, the East Kolkata Wetlands, India can accommodate 1450 million litres per day (MLD) of sewage, if the average depth of water is increased to 1.18 m.

/pdf/impact-of-enhanced-flow-on-the-flow-system-and-wastewater-18gmxttpag.pdf

Impact of enhanced flow on the flow system and wastewater characteristics of sewage-fed fisheries in India

The areas of investigation required to develop synthesis techniques for Boolean neural networks are discussed. Consideration is given to the problems of behavioral specification and to the lack of an analytic algebraic mapping between transformations within the hyper-state space and transformations within the function space of a network. Ways of identifying structure within both the behavior and functional architecture of classes of simple Boolean neural networks are suggested

Requirements for the synthesis of Boolean neural networks

A theory of neural networks

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A theory of neural networks