Interval Type-2 Neuro-fuzzy System Research Articles

Uniform Design-Based Interval Type-2 Neuro-fuzzy System and Its Performance Verification

In this paper, an interval type-2 neuro-fuzzy system with uniform design-based rule generation approach is proposed, and Begian–Melek–Mendel method is used for defuzzification. To the consequent learning, two least squares methods are involved in the consequent design for the interval type-2 neuro-fuzzy system, one is the recursive singular value decomposition, and the other is the weighted least squares estimator. Besides the interval type-2 neuro-fuzzy system modelling, another aim of this paper is to verify the interval type-2 neuro-fuzzy systems’ performance from the point of view of statistics, not just the average prediction accuracy or the best results from hundreds of iteration. With this in mind, a distribution-free least squares estimation method is used to assess the interval type-2 neuro-fuzzy system’s modelling capability.

Wind Speed Intervals Prediction using Meta-cognitive Approach

In this paper, an interval type-2 neural fuzzy inference system and its meta-cognitive learning algorithm for wind speed prediction is proposed. Interval type-2 neuro-fuzzy system is capable of handling uncertainty associated with the data and meta-cognition employs self-regulation mechanism for learning. The proposed system realizes Takagi-Sugeno-Kang inference mechanism and adopts a fast data-driven interval-reduction method. Meta-cognitive learning enables the network structure to evolve automatically based on the knowledge in data. The parameters are updated based on an extended Kalman filter algorithm. In addition, the proposed network is able to construct prediction intervals to quantify uncertainty associated with forecasts. For performance evaluation, a real-world wind speed prediction problem is utilized. Using historical data, the model provides short-term prediction intervals of wind speed. The performance of proposed algorithm is compared with existing state-of-the art fuzzy inference system approaches and the results clearly indicate its advantages in forecasting problems.

Interval type-2 neuro-fuzzy system with implication-based inference mechanism

Neuro-fuzzy systems have been proved to be an efficient tool for modelling real life systems. They are precise and have ability to generalise knowledge from presented data. Neuro-fuzzy systems use fuzzy sets – most commonly type-1 fuzzy sets. Type-2 fuzzy sets model uncertainties better than type-1 fuzzy sets because of their fuzzy membership function. Unfortunately computational complexity of type reduction in general type-2 systems is high enough to hinder their practical application. This burden can be alleviated by application of interval type-2 fuzzy sets. The paper presents an interval type-2 neuro-fuzzy system with interval type-2 fuzzy sets both in premises (Gaussian interval type-2 fuzzy sets with uncertain fuzziness) and consequences (trapezoid interval type-2 fuzzy set). The inference mechanism is based on the interval type-2 fuzzy Łukasiewicz, Reichenbach, Kleene-Dienes, or Brouwer–Gödel implications. The paper is accompanied by numerical examples. The system can elaborate models with lower error rate than type-1 neuro-fuzzy system with implication-based inference mechanism. The system outperforms some known type-2 neuro-fuzzy systems.

FPGA implementation of a new interval type-2 Beta neuro-fuzzy system with on-chip learning for image denoising application

Hardware implementation of conventional interval type-2 neuro-fuzzy systems with on-chip learning is essential for real time applications. However, existing implementations are resource consuming due to the complexity of their architectures and the use of iterative procedure for system output estimation. To overcome this problem, we propose a new interval type-2 neuro-fuzzy architecture. Accordingly, the number of layers is reduced owing to using Beta membership functions. Moreover, a simplified output computing operation is applied. For implementing Beta functions, an accurate and compact Centered Recursive Interpolation (CRI) method is used. For on-chip learning system, a new on-line incremental learning algorithm with gradient descent technique is applied to adjust its parameters. Furthermore, a synthesis of the corresponding design on a Field Programmable Gate Array (FPGA) platform is achieved in image denoising application. Performances comparison with the existing implementations shows the effectiveness of our chip in terms of resource requirements, speed and denoising performances.