# A self-organizing fuzzy neural network for sequence learning

## TL;DR

This paper introduces a self-organizing fuzzy neural network capable of online learning and reproducing multiple sequences simultaneously, combining sequence identification and location modules with a gradual learning process.

## Contribution

It presents a novel dynamic fuzzy neural network structure that learns multiple sequences online, integrating sequence identification and location for accurate reproduction.

## Key findings

- Successfully learns and reproduces multiple sequences simultaneously.
- Employs a gradual learning process with fuzzy rules and gradient descent.
- Demonstrates effectiveness in sequence learning tasks.

## Abstract

In this paper, a new self-organizing fuzzy neural network model is presented which is able to learn and reproduce different sequences accurately. Sequence learning is important in performing skillful tasks, such as writing and playing piano. The structure of the proposed network is composed of two parts: 1-sequence identifier which computes a novel sequence identity value based on initial samples of a sequence, and detects the sequence identity based on proper fuzzy rules, and 2-sequence locator, which locates the input sample in the sequence. Therefore, by integrating outputs of these two parts in fuzzy rules, the network is able to produce the proper output based on current state of the sequence. To learn the proposed structure, a gradual learning procedure is proposed. First, learning is performed by adding new fuzzy rules, based on coverage measure, using available correct data. Next, the initialized parameters are fine-tuned, by gradient descent algorithm, based on fed back approximated network output as the next input. The proposed method has a dynamic structure which is able to learn new sequences online. The proposed method is used to learn and reproduce different sequences simultaneously which is the novelty of this method.