IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation

• URL: http://arxiv.org/abs/2108.08704v1
• Date: Wed, 11 Aug 2021 13:00:13 GMT
• Title: IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation
• Authors: Armin Salimi-Badr
• Abstract summary: We introduce a new interval type-2 fuzzy neural network able to construct non-separable fuzzy rules with adaptive shapes. The proposed paradigm is successfully applied to real-world time-series predictions, regression problems, and nonlinear system identification.
• Score: 1.599072005190786
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, a new interval type-2 fuzzy neural network able to construct non-separable fuzzy rules with adaptive shapes is introduced. To reflect the uncertainty, the shape of fuzzy sets considered to be uncertain. Therefore, a new form of interval type-2 fuzzy sets based on a general Gaussian model able to construct different shapes (including triangular, bell-shaped, trapezoidal) is proposed. To consider the interactions among input variables, input vectors are transformed to new feature spaces with uncorrelated variables proper for defining each fuzzy rule. Next, the new features are fed to a fuzzification layer using proposed interval type-2 fuzzy sets with adaptive shape. Consequently, interval type-2 non-separable fuzzy rules with proper shapes, considering the local interactions of variables and the uncertainty are formed. For type reduction the contribution of the upper and lower firing strengths of each fuzzy rule are adaptively selected separately. To train different parameters of the network, the Levenberg-Marquadt optimization method is utilized. The performance of the proposed method is investigated on clean and noisy datasets to show the ability to consider the uncertainty. Moreover, the proposed paradigm, is successfully applied to real-world time-series predictions, regression problems, and nonlinear system identification. According to the experimental results, the performance of our proposed model outperforms other methods with a more parsimonious structure.

Related papers

• A New Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction [9.546043411729206]
  This paper proposes a new self-organizing interval type-2 fuzzy neural network with multiple outputs (SOIT2FNN-MO) for multi-step time series prediction. A nine-layer network is developed to improve prediction accuracy, uncertainty handling and model interpretability.
  arXiv  Detail & Related papers  (2024-07-10T19:35:44Z)
• Efficient Interpretable Nonlinear Modeling for Multiple Time Series [5.448070998907116]
  This paper proposes an efficient nonlinear modeling approach for multiple time series. It incorporates nonlinear interactions among different time-series variables. Experimental results show that the proposed algorithm improves the identification of the support of the VAR coefficients in a parsimonious manner.
  arXiv  Detail & Related papers  (2023-09-29T11:42:59Z)
• Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification [14.20513951604573]
  We propose a multi-label classification model based on interval type-2 fuzzy logic. In the proposed model, we use a deep neural network to predict the type-1 fuzzy membership of an instance. We also propose a loss function to measure the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model.
  arXiv  Detail & Related papers  (2023-02-21T04:00:44Z)
• UNFIS: A Novel Neuro-Fuzzy Inference System with Unstructured Fuzzy Rules for Classification [1.0660480034605238]
  This paper presents a neuro-fuzzy inference system for classification applications. It can select different sets of input variables for constructing each fuzzy rule. It has better or very close performance with a parsimonious structure consisting of unstructured fuzzy.
  arXiv  Detail & Related papers  (2022-10-28T17:51:50Z)
• Training Recurrent Neural Networks by Sequential Least Squares and the Alternating Direction Method of Multipliers [0.20305676256390928]
  We propose the use of convex and twice-differentiable loss and regularization terms for determining optimal hidden network parameters. We combine sequential least squares with alternating direction multipliers. The performance of the algorithm is tested in a nonlinear system identification benchmark.
  arXiv  Detail & Related papers  (2021-12-31T08:43:04Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• Convolutional Filtering and Neural Networks with Non Commutative Algebras [153.20329791008095]
  We study the generalization of non commutative convolutional neural networks. We show that non commutative convolutional architectures can be stable to deformations on the space of operators.
  arXiv  Detail & Related papers  (2021-08-23T04:22:58Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model [72.57056258027336]
  In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules. We introduce an exponential weight approach inspired by the weight function theory encountered in harmonic analysis.
  arXiv  Detail & Related papers  (2020-07-02T15:42:15Z)
• Learn to Predict Sets Using Feed-Forward Neural Networks [63.91494644881925]
  This paper addresses the task of set prediction using deep feed-forward neural networks. We present a novel approach for learning to predict sets with unknown permutation and cardinality. We demonstrate the validity of our set formulations on relevant vision problems.
  arXiv  Detail & Related papers  (2020-01-30T01:52:07Z)
• Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation [77.7420231319632]
  We adapt contextual generation of categorical sequences to a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control. We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios.
  arXiv  Detail & Related papers  (2019-12-31T03:01:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.