Research on autonomous artificial neural network

Research on neural network has witnessed impressive progress on its model, learning algorithm and application. Owe to the ambiguity, fuzziness and ability of approximating functions of any complexity it provides, neural computing method has gained great success in various fields. However, serious limitations remain accompanying the method concerning accessibility, flexibility, scaling and reliability. Efforts have been made to relieve the problem, of which decomposition approach is typically used. Although it works, the method is greatly confined by the decomposition strategy and scalability of such system which is scarified. Previous researches focus on effectively dividing the input space to build up modular systems for given tasks and that the design of the modules themselves is neglected. In this paper, we introduce the notion of autonomous artificial neural network (AANN) for the first time and use AANN units as the basic building block of a modular neural system. The difference between AANN units and conventional neural network is that AANN units possess the ability of self-reflection. Unlike conventional neural networks, which directly give an output corresponding to an input without commenting on its quality, an AANN unit comments in its own point of view on the output, providing extra information such as whether the result is correct or not or to what degree the result can be believed. With AANN units as building blocks, a modular neural system is greatly relieved from what it suffers. Scalability, reliability and flexibility of such a system are greatly improved. We have also proposed a coding method to implement AANN units. And a neural network ensemble of AANN units is built up, which acquires knowledge progressively by inheriting the learned knowledge of AANN units attached to it.