A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system

Subramanian, K. R.; Sundaram, Suresh

doi:10.1016/j.asoc.2012.06.012

Cited by 111 publications

(85 citation statements)

References 32 publications

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“…Of these various models, the model proposed by Nelson and Narens [19] is the most comprehensive and imitable in machine learning. Several machine learning algorithms have been developed based on this Nelson and Narens model of human meta-cognition for real-valued neural networks [20,12], complex-valued neural networks [21][22][23] and neuro-fuzzy inference systems [24,25]. It can be seen from these studies that a meta-cognitive network with self-regulated learning outperform the networks without meta-cognition.…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images

Mahanand¹,

Ramasamy

Sundaram

2013

Lecture Notes in Computer Science

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Abstract. This paper presents a pilot study on the development of an automated diagnostic tool for Attention Deficiency Hyperactivity Disorder (ADHD) based on regional anatomy of the child brain. For the pilot study, amygdala and cerebellar vermis are chosen from magnetic resonance images obtained from ADHD-200 consortium data set. These regions play a vital role in the control of emotional response and behavior/locomotion, respectively. The images are preprocessed, registered by transforming each image to the space of the population average. The gray matter tissue probability values of amygdala and cerebellar vermis are obtained by applying a region-of-interest mask. These values are then used to train a Projection Based Learning algorithm for a Metacognitive Radial Basis Function Network (PBL-McRBFN) for the diagnosis of ADHD and prediction of its subtype. Performance results show that the PBL-McRBFN diagnoses ADHD and predicts its subtypes based on these regions with an accuracy of approx. 65% and 62%, respectively.

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Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images

Mahanand¹,

Ramasamy

Sundaram

2013

Lecture Notes in Computer Science

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“…Metacognitive Neural Network [22], Meta-Cognitive NeuroFuzzy Inference System [23] and Meta-cognitive Fully Complex-valued Relaxation Network [24] developed from a simple meta-cognitive model [25] which have been evaluated and shown superior performance against the well-known classifiers. In this study, meta-cognitive online sequential extreme learning machine (MCOS-ELM) that combined OS-ELM with meta-cognitive strategy is originally proposed to predict the minority class of PM 10 level, whose data arrives sequentially.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a learning principle for data imbalance problem called meta-cognitive strategy [22][23][24] has been applied to different machine learning algorithms. Metacognitive Neural Network [22], Meta-Cognitive NeuroFuzzy Inference System [23] and Meta-cognitive Fully Complex-valued Relaxation Network [24] developed from a simple meta-cognitive model [25] which have been evaluated and shown superior performance against the well-known classifiers.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Vong

Chiu

et al. 2014

Cogn Comput

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Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

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“…A family of eTS models, such as simplified eTS model (Simp eTS) [10], evolving extended TS model (exTS) [2], and evolving TS model from streaming data (eTS+) [11] have been developed. In a similar way, a meta-cognitive neuro-fuzzy inference system (McFIS) [12] is proposed to update the rule base based on the spherical potential of each learning datum that represents the novel knowledge contained in each datum.…”

Section: Introductionmentioning

confidence: 99%

Correntropy-Based Evolving Fuzzy Neural System

Bao

Rong

Angelov

et al. 2018

IEEE Trans. Fuzzy Syst.

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Abstract-In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the nonGaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

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A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system

Cited by 111 publications

References 32 publications

Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images

Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Correntropy-Based Evolving Fuzzy Neural System

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