A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems

Leng, Gang; Zeng, Xiao‐Jun; Keane, John

doi:10.1016/j.asoc.2009.05.006

Cited by 26 publications

(13 citation statements)

References 27 publications

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“…Table 1 lists results obtained by five methods: the traditional ANFIS (training by gradient descent back-propagation algorithm), ANFIS-EM-EKS, an adaptive neural network (ANN) trained by a scaled conjugate gradient algorithm, support vector machines (SVM) using Gaussian Fig. 3 Deviations in rule regions: a the grid partition of a two-dimensional input space, b the higher deviations of r jk , c the lower deviations of r jk , d the lower deviations of l jk , e the higher deviations of l jk kernels, and the self-adaptive neuro-fuzzy system (SANFS) [36]. The RMSE and APE values obtained by these methods are given in Table 1, where ANFIS-EM-EKS produces the lowest RMSE and APE values and allows rapid convergence to local optimal values.…”

Section: Two-input Nonlinear Sinc Function Approximationmentioning

confidence: 99%

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

Çetişli

Edizkan

2010

Neural Comput & Applic

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In this paper, we propose a new supervised learning method for adaptive neuro-fuzzy inference system (ANFIS) training, which uses the expectation maximization (EM) algorithm and extended Kalman smoother (EKS) together; we refer to it here as the EM-EKS training method. While the EKS tunes the ANFIS parameters, the EM algorithm estimates the parameters of the Kalman filter and avoids non-optimal performance. Besides, we also propose a new algorithm to select the initial values of the EKS parameters. We compare the EM-EKS method of ANFIS training with traditional ANFIS training. Although the new training method requires more computing time, it yields improved RMSE values in function approximation and prediction problems. Examples of benchmark function approximation and prediction illustrate the effectiveness of the EM-EKS ANFIS training method.

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Section: Two-input Nonlinear Sinc Function Approximationmentioning

confidence: 99%

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

Çetişli

Edizkan

2010

Neural Comput & Applic

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“…through local element tuning. Leng et al proposed an algorithm for the generation of Takagi-Sugenotype (TS) neuro fuzzy systems 20 . The algorithm consists of two stages: in the first stage, an initial structure is adapted from an empty neuron or fuzzy rule set, based on the geometric growth criteron and the ε-completeness of fuzzy rules; then, in the second stage, the initial structure is refined through a hybrid learning algorithm.…”

Section: The State Of the Art Of Constructive Methodsmentioning

confidence: 99%

“…The training samples of the first synthetic experiment 32 , consists of 216 uniformly sampled three-input data from input ranges In the third synthetic experiment 20 , we tested our approach with the well-known Mackey Glass Timeseries, with the same experimental configuration. The training data are 1000 input-target data generated between t = 124 and 1123, and another 1000 input-target data between t = 1124 and 2123.…”

Section: Datasetsmentioning

confidence: 99%

SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

Allende‐Cid¹,

Salas²,

Veloz³

et al. 2016

IJCIS

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This paper presents a new adaptive learning algorithm to automatically design a neural fuzzy model. This constructive learning algorithm attempts to identify the structure of the model based on an architectural self-organization mechanism with a data-driven approach. The proposed training algorithm self-organizes the model with intuitive adding, merging and splitting operations. Sub-networks compete to learn specific training patterns and, to accomplish this task, the algorithm can either add new neurons, merge correlated ones or split existing ones with unsatisfactory performance. The proposed algorithm does not use a clustering method to partition the input-space like most of the state of the art algorithms. The proposed approach has been tested on well-known synthetic and real-world benchmark datasets. The experimental results show that our proposal is able to find the most suitable architecture with better results compared with those obtained with other methods from the literature.

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“…more recent approach is the inference model based on a combination of adaptive neural networks and fuzzy logic (Leng et al, 2009).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Multi-faceted assessment of trademark similarity

Setchi

Anuar

2016

Expert Systems with Applications

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Highlights A novel method for the assessment of trademark similarity is proposed. The method blends together visual, semantic and phonetic similarity. It produces an aggregated score based on the individual assessments. Evaluation using information retrieval measures and human judgment.ACCEPTED MANUSCRIPT One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.

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A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems

Cited by 26 publications

References 27 publications

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

Estimation of adaptive neuro-fuzzy inference system parameters with the expectation maximization algorithm and extended Kalman smoother

SONFIS: Structure Identification and Modeling with a Self-Organizing Neuro-Fuzzy Inference System

Multi-faceted assessment of trademark similarity

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