Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets

Bhattacharya, Diptendu; Konar, Amit; Das, Pratyusha

doi:10.1016/j.neucom.2015.06.073

Cited by 34 publications

(9 citation statements)

References 43 publications

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“…In the first (training) phase, a new computational model of type-2 fuzzy regression (reasoning followed by defuzzification) is developed to fit the brain (hemodynamic) response of the subject to an aromatic stimulus as the input, and the oral response of the subject about the qualitative degree of concentration perceived by him/her in the form: Very Low, Low, Medium, High and Very High as the output. For convenience of realization, each qualitative grade is defined as sub-intervals of [0, 100], such as [1,20) for Very Low, [20,40) for Low, [40,60) for Medium, [60,80) for High, and [80, 100] for Very High. Intervals of width 20, instead of absolute value in [0, 100] is utilized to express subject's perceptual response to avoid extensive subject's training for each degree of concentration in [0, 100].…”

Section: Principles and Methodologymentioning

confidence: 99%

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Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Laha

Konar

Rakshit

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Olfactory perceptual degradation refers to the inability of people to recognize the variation in concentration levels of olfactory stimuli. The paper attempts to assess the degree of olfactory perceptual degradation of subjects from their hemodynamic response to olfactory stimuli. This is done in 2 phases. In the first (training) phase, a regression model is developed to assess the degree of concentration levels of an olfactory stimulus by a subject from her hemodynamic response to the stimulus. In the second (test) phase, the model is employed to predict the possible concentration level experienced by the subject in [0, 100] scale. The difference between the modelpredicted response and the oral response (the center value of the qualitative grades) of the subject about her perceived concentration level is regarded as the quantitative measure of the degree of subject's olfactory degradation. The novelty of the present research lies in the design of a General Type-2 fuzzy regression model, which is capable of handling uncertainty due to the presence of intra-and inter-session variations in the brain responses to olfactory stimuli. The attractive feature of the paper lies in adaptive tuning of secondary membership functions to reduce model prediction error in an evolutionary optimization setting. The effect of such adaptation in secondary measures is utilized to adjust the corresponding primary memberships in order to reduce the uncertainty involved in the regression process. The proposed regression model has good prediction accuracy and high time-efficiency as evident from average percentage success rate (PSR) and run-time complexity analysis respectively. The Friedman test undertaken also confirms the superior performance of the proposed technique with other competitive techniques at 95% confidence level.

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Section: Principles and Methodologymentioning

confidence: 99%

“…5. Represent the actual concentrations of the stimuli presented in [0, 100], and obtain the measure of grades of the stimuli as Very Low, Low, Medium, High, Very High based on the measure of the estimated concentration respectively in [1,20), [20,40), [40,60), [60,80), [80, 100] ranges. 6.…”

Section: Experiments 1: Subject Familiarity With Stimulus Concentrationmentioning

confidence: 99%

Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Laha

Konar

Rakshit

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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“…Naturally, the superimposed stochastic noise yields erroneous results in mapping, if realized with classical mapping techniques, such as neural functional approximation [55][56], nonlinear regression [57] and the like. Fuzzy logic has shown promising performance in functional mapping in presence of noisy measurements because of their inherent nonlinearity in the MFs (Gaussian/Triangular) [78]. The effect of measurement noise in functional mapping is reduced further in T2FS [77] because of its characteristic to handle intra-personal level uncertainty due to the presence of stochastic noise.…”

Section: B Type-2 Fuzzy Mapping and Parameter Adaptation By Perceptrmentioning

confidence: 99%

Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

Ghosh

Konar

Rakshit

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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The paper attempts to model short-term memory (STM) for shape-reconstruction tasks by employing a 4-stage deep brain leaning network (DBLN), where the first 2 stages are built with Hebbian learning and the last 2 stages with Type-2 Fuzzy logic. The model is trained stage-wise independently with visual stimulus of the object-geometry as the input of the first stage, EEG acquired from different cortical regions as input and output of respective intermediate stages, and recalled object-geometry as the output of the last stage. Two error feedback loops are employed to train the proposed DBLN. The inner loop adapts the weights of the STM based on a measure of error in model-predicted response with respect to the object-shape recalled by the subject. The outer loop adapts the weights of the iconic (visual) memory based on a measure of error of the model predicted response with respect to the desired object-shape. In the test phase, the DBLN model reproduces the recalled object shape from the given input object geometry. The motivation of the paper is to test the consistency in STM encoding (in terms of similarity in network weights) for repeated visual stimulation with the same geometric object. Experiments undertaken on healthy subjects, yield high similarity in network weights, whereas patients with pre-frontal lobe Amnesia yield significant discrepancy in the trained weights for any two trials with the same training object. This justifies the importance of the proposed DBLN model in automated diagnosis of patients with learning difficulty. The novelty of the paper lies in the overall design of the DBLN model with special emphasis to the last 2 stages of the network, built with vertical slice based type-2 fuzzy logic, to handle uncertainty in function approximation (with noisy EEG data). The proposed technique outperforms the state-of-the-art functional mapping algorithms with respect to the (pre-defined outer loop) error metric, computational complexity and runtime.

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“…In recent years, variety of methods has been suggested for training type-2 fuzzy neural networks such as Genetic Algorithm (GA) [18] and Particle Swarm Optimization (PSO) [19]. By daily growing research on type-2 fuzzy systems, these systems have found excessive applications such as time series prediction [20], linear motor control [21], system identification and modeling [22], sliding mode control [23], pattern recognition [24], and robot control [25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

Asad

Shamsi

Ivani

et al. 2016

International Journal on Smart Sensing and Intelligent Systems

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Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets

Cited by 34 publications

References 43 publications

Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

Adaptive Intelligent Inverse Control Of Nonlinear Systems With Regard To Sensor Noise And Parameter Uncertainty (Magnetic Ball Levitation System Case Study)

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