ABSTRACT

‘Neuro Language Generator using Finite State Machine’ is based on neural network and finite state machine. The fundamental properties of neural network along with the power of Turing machine prove how it can be implemented for formal language processing. This paper elaborates the conventional dynamical language generators, limitations of the conventional dynamical language generators and proposes a new architecture for formal language processing. The conventional dynamical language generators used for neural language generators is feedforward RNN. It expresses dynamical language generator using finite automaton and dynamical language generator using pushdown automaton. Conventional dynamical generators tend to have stability problem, incapable of network training and lack of memory. It is proposed that the new method ‘Neuro Language Generator using Finite State Machine’ solves most of the problems, which the traditional methods fail to do. The approach employs finite state technology for a RNN in the task of learning to achieve stability in network structure. RNN architecture performs the same computation as a Turing machine. The RNN architecture acts as a language generator, which accepts formal language. Neuro Language Generator is a RNN that uses feedback connections. NLG can be used to solve more complicated problems compared to traditional dynamical generator.