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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, ...
Using consensus ensembles to identify suspect data
In a consensus ensemble all members must agree before they classify a data point. But even when they all agree some data is still misclassified. In this paper we look closely at consistently misclassified data to investigate ...
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 ...
Development of a Māori database for speech perception and generation
Māori speech data collection and analysis is an ongoing process, as new and existing data sets are continuously accessed for many different experimental speech perception and generation processing tasks. A data management ...
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. ...
Evolving fuzzy neural networks for on-line knowledge discovery
Fuzzy neural networks are connectionist systems that facilitate learning from data, reasoning over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept evolving fuzzy neural networks (EFuNNs), ...
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 ...