Meta-cognitive Learning Algorithm Research Articles

Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system

While significant efforts for online learning have been devoted to arrive at reliable predictions of crisp values, the problem of prediction interval (PI) in practical data is one of the underexplored areas in the existing literature. PI aims to produce upper and lower bound predictions which capture possible domain solution. This paper aims to extend a prominent meta-cognitive learning algorithm, namely meta-cognitive interval type-2 fuzzy inference system (McIT2FIS), to cope with the problem of prediction interval in real-time. McIT2FIS is constructed under interval type-2 fuzzy inference system and realizes the meta-cognitive learning theory featuring the basic three elements of human learning: what-to-learn, how-to-learn, when-to-learn. Unlike existing works in PI, McIT2FIS-PI works fully in the online mode and is capable of performing automatic knowledge acquisition from data streams. The efficacy of McIT2FIS-PI has been experimentally validated in a real-world wave characteristics prediction in Semakau Island, Singapore, where it is capable of delivering accurate short-term prediction intervals of wave parameters. The performance of McIT2FIS-PI is also compared with existing state-of-the-art fuzzy inference systems in benchmark problems where it attains competitive accuracy while retaining comparable complexity.

Wave Forecasting using Meta-cognitive Interval Type-2 Fuzzy Inference System

Renewable energy is fast becoming a mainstay in today’s energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently essential in coastal and ocean engineering studies. However, it is difficult to predict wave parameters in long term and even in the short term due to its intermittent nature. This study aims to propose a solution to handle the issue using Interval type-2 fuzzy inference system, or IT2FIS. IT2FIS has been shown to be capable of handling uncertainty associated with the data. The proposed IT2FIS is a fuzzy neural network realizing Takagi-Sugeno-Kang inference mechanism employing meta-cognitive learning algorithm. The algorithm monitors knowledge in a sample to decide an appropriate learning strategy. Performance of the system is evaluated by studying significant wave heights obtained from buoys located in Singapore. The results compared with existing state-of-the art fuzzy inference system approaches clearly indicate the advantage of IT2FIS based wave prediction.

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.

An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm

In this paper, we propose an evolving interval type-2 neuro-fuzzy inference system and its meta-cognitive learning algorithm. The antecedent of each rule is an interval type-2 fuzzy set and the consequent realizes the Takagi–Sugeno–Kang inference mechanism. It employs a data driven mechanism for type-reduction and uncertainty is modeled using both the antecedent and consequent parameters. The learning starts with zero rules and evolves the network parameters and structure as a new sample arrives, using a meta-cognitive learning algorithm. The meta-cognitive learning algorithm regulates learning by employing adequate learning strategies. It uses gradient-descent to adapt the network parameters. The proposed algorithm is referred to as meta-cognitive interval type-2 neuro-fuzzy inference system-gradient descent (McIT2FIS-GD). The performance of McIT2FIS-GD is evaluated using a set of benchmark and a real-world wind speed prediction problem and compared with existing type-1 and type-2 systems. The results highlight that performance of McIT2FIS-GD is superior than other compared algorithms.

An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm

In this paper, we propose an evolving interval type-2 neurofuzzy inference system (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval type-2 fuzzy sets in the antecedent part of each rule and the consequent realizes Takagi–Sugeno–Kang fuzzy inference mechanism. In order to render the inference fast and accurate, we propose a data-driven interval-reduction approach to convert interval type-1 fuzzy set in antecedent to type-1 fuzzy number in the consequent. During learning, the sequential algorithm learns a sample one-by-one and only once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the learning process by appropriate selection of learning strategies and helps the proposed IT2FIS to approximate the input–output relationship efficiently. An evolving IT2FIS employing a metacognitive learning algorithm is referred to as McTI2FIS. Performance of metacognitive interval type-2 neurofuzzy inference system (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existing type-2 and type-1 fuzzy inference systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a practical stock price-tracking problem. The results clearly highlight that McIT2FIS performs better than other existing results in the literature.

Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network

In this paper, we present an accurate detection of Autism Spectrum Disorder (ASD) from structural MRI using an Extended Metacognitive Radial Basis Function Neural Classifier (EMcRBFN). An automatic whole brain Voxel Based Morphometry (VBM) approach is used to identify gray matter composition in the brain from structural Magnetic Resonance Imaging (MRI) and an improved q-Gaussian classifier and its metacognitive learning algorithm has been proposed to approximate the functional relationship between the high dimensional VBM features and the true class labels. Recent genetic studies indicate that ASD manifests in different ways between males and females and also between adolescents and adults. Accordingly, the proposed EMcRBFN classifier has been evaluated using the publicly available Autism Brain Imaging Data Exchange dataset with a comprehensive study on both males and females and also between adolescents and adults in both categories. EMcRBFN classifier performance is compared with currently existing results for ASD classification in the literature and also with well known standard classifiers. The results clearly indicate that the performance of the EMcRBFN classifier is better than that of the other classifiers considered in this study. Further, the comprehensive study also indicates that the following subregions in the brain viz., premotor cortex and supplementary motor cortex are affected for adult-females while the somatosensory cortex subregion is affected for adolescent-females with ASD. Similar results indicate that the precentral gyrus, motor cortex, medial frontal gyrus and the paracentral lobule areas are affected for adolescent males while the superior frontal gyrus and the frontal eye fields areas are affected for adult males with ASD.

A complex-valued neuro-fuzzy inference system and its learning mechanism

In this paper, we present a complex-valued neuro-fuzzy inference system (CNFIS) and develop its meta-cognitive learning algorithm. CNFIS has four layers – an input layer with m rules, a Gaussian layer with K rules, a normalization layer with K rules and an output layer with n rules. The rules in the Gaussian layer map the m-dimensional complex-valued input features to a K-dimensional real-valued space. Hence, we use the Wirtinger calculus to obtain the complex-valued gradients of the real-valued function in deriving the learning algorithm of CNFIS. Next, we also develop the meta-cognitive learning algorithm for CNFIS referred to as “meta-cognitive complex-valued neuro-fuzzy inference system (MCNFIS)”. CNFIS is the cognitive component of MCNFIS and a self-regulatory learning mechanism that decides what-to-learn, how-to-learn, and when-to-learn in a meta-cognitive framework is its meta-cognitive component. Thus, for every epoch of the learning process, the meta-cognitive component decides if each sample in the training set must be deleted or used to update the parameters of CNFIS or to be reserved for future use.The performances of CNFIS and MCNFIS are studied on a set of approximation and real-valued classification problems, in comparison to existing complex-valued learning algorithms in the literature. First, we evaluate the approximation performances of CNFIS and MCNFIS on a synthetic complex-valued function approximation problem, an adaptive beam-forming problem and a wind prediction problem. Finally, we study the decision making performance of CNFIS and MCNFIS on a set of benchmark real-valued classification problems from the UCI machine learning repository. Performance study results on approximation and real-valued classification problems show that CNFIS and MCNFIS outperform existing algorithms in the literature.

Sequential projection-based metacognitive learning in a radial basis function network for classification problems.

In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.

HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network

This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-valued neural network called "Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN)". McFCRN has two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Relaxation Network (FCRN) with a fully complex-valued Gaussian like activation function (sech) in the hidden layer and an exponential activation function in the output layer forms the cognitive component. The meta-cognitive component contains a self-regulatory learning mechanism which controls the learning ability of FCRN by deciding what-to-learn, when-to-learn and how-to-learn from a sequence of training data. The input parameters of cognitive components are chosen randomly and the output parameters are estimated by minimizing a logarithmic error function. The problem of explicit minimization of magnitude and phase errors in the logarithmic error function is converted to system of linear equations and output parameters of FCRN are computed analytically. McFCRN starts with zero hidden neuron and builds the number of neurons required to approximate the target function. The meta-cognitive component selects the best learning strategy for FCRN to acquire the knowledge from training data and also adapts the learning strategies to implement best human learning components. Performance studies on a function approximation and real-valued classification problems show that proposed McFCRN performs better than the existing results reported in the literature.