Hybrid Neural Network Cerebellar Model Articulation Controller Design for Non-linear Dynamic Time-Varying Plants

Le, Tien-Loc; Lin, Chih-Min; Hong, Sung-Kyung

doi:10.3389/fnins.2020.00695

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…This conjecture based on the structure was directly applied to a cerebellar model articulation controller (CMAC; Albus, 1975 ), which is based on the fact that the cerebellum is involved in smooth motor control. CMAC is still utilized with modifications (Tsa et al, 2018 ; Le et al, 2020 ). Because the cerebellum is not a sole motor controller, the whole motor control process should be analyzed by including the initial command generator and motor plant.…”

Section: Outcome Of Optimization: Network Architecturementioning

confidence: 99%

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Jeon

Kim

2023

Front. Comput. Neurosci.

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Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

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Section: Outcome Of Optimization: Network Architecturementioning

confidence: 99%

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Jeon

Kim

2023

Front. Comput. Neurosci.

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“…Their advantages lie in the possibility to consider the best aspects of discriminative and generative models. For instance, a hybrid architecture can adopt small inputs to avoid the problem of determining the right network size and instead an increasing number of neurons in receptive-field spaces [106]. At the same time, by a proper enhancement of the initial weights through suitable algorithms, neural networks in hybrid architectures can provide higher accuracy and predictive power [107,108].…”

Section: Machine Learning Neural Network and Deep Learningmentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Quynh

Long

et al. 2020

IEEE Access

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In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented. A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network. The gradient-descent method is used to derive the adaptive laws for online updating the network parameters. Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm. Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules. The control system's exponential stability is analyzed by applying the Lyapunov theory. Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method. INDEX TERMS self-organizing algorithm, type-2 fuzzy CMAC, Jaya algorithm, function-link network, asymmetric membership function.

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Hybrid Neural Network Cerebellar Model Articulation Controller Design for Non-linear Dynamic Time-Varying Plants

Cited by 7 publications

References 29 publications

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

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