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Looking for a new AI paradigm: Evolving connectionist and fuzzy connectionist systems—Theory and applications for adaptive, on-line intelligent systems
The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, ...
Dynamic evolving fuzzy neural networks with `m-out-of-n' activation nodes for on-line adaptive systems
The paper introduces a new type of evolving fuzzy neural networks (EFuNNs), denoted as mEFuNNs, for on-line learning and their applications for dynamic time series analysis and prediction. mEFuNNs evolve through incremental, ...
Neuro-fuzzy engineering for spatial information processing
This paper proposes neuro-fuzzy engineering as a novel approach to spatial data analysis and for building decision making systems based on spatial information processing, and the development of this approach by the authors ...
Adaptive, evolving, hybrid connectionist systems for image pattern recognition
The chapter presents a new methodology for building adaptive, incremental learning systems for image pattern classification. The systems are based on dynamically evolving fuzzy neural networks that are neural architectures ...
Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition
The paper is a study on a new class of spatial-temporal evolving fuzzy neural network systems (EFuNNs) for on-line adaptive learning, and their applications for adaptive phoneme recognition. The systems evolve through ...
Evolving connectionist systems for on-line, knowledge-based learning: Principles and applications
The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. ...
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent ...
A fuzzy neural network model for the estimation of the feeding rate to an anaerobic waste water treatment process
Biological processes are among the most challenging to predict and control. It has been recognised that the development of an intelligent system for the recognition, prediction and control of process states in a complex, ...
Connectionist-based information systems: a proposed research theme
General Characteristics of the Theme
• Emerging technology with rapidly growing practical applications
• Nationally and internationally recognised leadership of the University of Otago
• Already established organisation ...
From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition
This paper discusses the problem of adaptation in automatic speech recognition systems (ASRS) and suggests several strategies for adaptation in a modular architecture for speech recognition. The architecture allows for ...