Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends

Widiputra, Harya; Pears, Russel; Kasabov, Nikola

doi:10.1007/978-3-642-20847-8_14

Cited by 14 publications

(15 citation statements)

References 10 publications

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“…Elements of ECOS have been proposed as part of the classical NN models, such as SOM, RBF, FuzyARTMap, Growing neural gas, neuro-fuzzy systems, RAN (see [22,24]). Other ECOS models, along with their applications, have been reported in [7,24,31,32]. The principle of ECOS is for neurons to be allocated as centres of fuzzy data clusters and for the system to create local models in these clusters.…”

Section: Fuzzy Neurons and Fuzzy Neural Network Evolving Connectionmentioning

confidence: 99%

The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

Kasabov

2013

On Fuzziness

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This chapter follows the development of a class of intelligent information systems called evolving neuro-fuzzy systems (ENFS). ENFS combine the adaptive/evolving learning ability of neural networks and the approximate reasoning and linguistically meaningful explanation features of fuzzy rules. The review includes fuzzy expert systems, fuzzy neuronal networks, evolving connectionist systems, spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point of few of fuzzy rule interpretation as new knowledge acquired during their adaptive/evolving learning. This review is based on the author's personal (evolving) research, integrating principles from neural networks, fuzzy systems and nature. Early work on the integration of neural networks and fuzzy systems for knowledge engineering: Neuro-fuzzy expert systemsThe seminal work by Lotfi Zadeh on fuzzy sets, fuzzy rules and intelligent systems [36][37][38] opened the field for the creation of new types of expert systems that combined the learning ability of neural networks, at a lower level of information processing, and the reasoning and explanation ability of fuzzy rule-based systems, at the higher level. An exemplar system is shown in Figure 22.1, where at a lower level a neural network (NN) module predicts the level of a stock index and a fuzzy reasoning module combines the predicted values with some macro-economic variables, using the following types of fuzzy rules [18]:IF AND THEN (22.1) These fuzzy expert systems continued the development of the hybrid NN-rulebased expert systems that used crisp propositional and fuzzy rules [13,15,17]. They represented a major topic at some conferences (Figure 22.2).

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Section: Fuzzy Neurons and Fuzzy Neural Network Evolving Connectionmentioning

confidence: 99%

The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

Kasabov

2013

On Fuzziness

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“…The states can be further classified into pre-define classes (e.g. buy, hold, sell, invest, likely bankruptcy) [113]; -Personalized modelling, which is concerned with the creation of a single model for an individual input data [58,59,62]. Here as an individual data a whole SSTD pattern is taken rather than a single vector.…”

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Kasabov

2012

Advances in Computational Intelligence

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“…bioinformatics; speech and image processing; multimodal audio-visual information processing; ecological modelling; robot control; personalised modelling [28,32]; neuroinformatics and brain study; financial applications [54]. Software environment Neucom (www.theneucom.com) has been developed to include some of the ECOS methods.…”

Section: Evolving Connectionist Systems (Ecos)mentioning

confidence: 99%

“…-Cognitive and emotional robotics [3] and neuro-rehabilitation robots [30]; -Modelling the progression or the response to treatment of neurodegenerative diseases [30]; -Modelling financial and economic problems (neuro-economics) [54]; -Personalized modeling [32]; -Neuromorphic computation [18,9,51].…”

Section: Current and Future Applications Of Esnn For Stprmentioning

confidence: 99%

Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition

Kasabov

2012

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS

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This paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted. AbstractThis paper provides a survey on the evolution of the evolving connectionist systems (ECOS) paradigm, from simple ECOS introduced in 1998 to evolving spiking neural networks (eSNN) and neurogenetic systems. It presents methods for their use for spatio-and spectro temporal pattern recognition. Future directions are highlighted.

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Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends

Cited by 14 publications

References 10 publications

The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Evolving spiking neural networks for spatio-and spectro-temporal pattern recognition

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