Distributed Activation, Search, and Learning by ART and ARTMAP Neural Networks

Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation can cause problems such as category proliferation with fast learning and a noisy training set. A new class of ART models with an arbitrarily distributed code representation is outlined here. With winner-take-all coding, the unsupervised distributed ART model (dART) reduces to fuzzy ART and the supervised distributed ARTMAP model (dARTMAP) reduces to fuzzy ARTMAP. dART automatically apportions learned changes according to the degree of activation of each node, which permits fast as well as slow learning with compressed or distributed codes. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Dynamic weights that project to coding nodes obey a distributed instar leaning law and those that originate from coding nodes obey a distributed outstar learning law. Inputs activate distributed codes through phasic and tonic signal components with dual computational properties, and a parallel distributed match-reset-search process helps stabilize memory.National Science Foundation (IRI 94-0 1659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

Adaptive Resonance: An Emerging Neural Theory of Cognition

Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, neural, computational, and technological domains. Minimal models provide a conceptual framework, for formulating questions about the nature of cognition; an architectural framework, for mapping cognitive functions to cortical regions; a semantic framework, for precisely defining terms; and a computational framework, for testing hypotheses. These systems are here exemplified by the distributed ART (dART) model, which generalizes localist ART systems to allow arbitrarily distributed code representations, while retaining basic capabilities such as stable fast learning and scalability. Since each component is placed in the context of a unified real-time system, analysis can move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness. Local design is driven by global functional constraints, with each network synthesizing a dynamic balance of opposing tendencies. The self-contained working ART and dART models can also be transferred to technology, in areas that include remote sensing, sensor fusion, and content-addressable information retrieval from large databases.Office of Naval Research and the defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (20-316-4304-5

Distributed ART Networks for Learning, Recognition, and Prediction

Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation can cause problems such as category proliferation with fast learning and a noisy training set. A new class of ART models with an arbitrarily distributed code representation is outlined here. With winner-take-all coding, the unsupervised distributed ART model (dART) reduces to fuzzy ART and the supervised distributed ARTMAP model (dARTMAP) reduces to fuzzy ARTMAP. dART automatically apportions learned changes according to the degree of activation of each node, which permits fast as well as slow learning with compressed or distributed codes. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Dynamic weights that project to coding nodes obey a distributed instar leaning law and those that originate from coding nodes obey a distributed outstar learning law. Inputs activate distributed codes through phasic and tonic signal components with dual computational properties, and a parallel distributed match-reset-search process helps stabilize memory.National Science Foundation (IRI 94-0 1659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Adaptive Tesselation CMAC

An ndaptive tessellation variant of the CMAC architecture is introduced. Adaptive tessellation is an error-based scheme for distributing input representations. Simulations show that the new network outperforms the original CMAC at a vnriety of learning tasks, including learning the inverse kinematics of a two-link arm.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100); National Science Foundation (IRI-90-00530); Boston University Presidential Graduate Fellowshi

An investigation into the performance and representation of a stochastic evolutionary neural tree

Copyright Springer.The Stochastic Competitive Evolutionary Neural Tree (SCENT) is a new unsupervised neural net that dynamically evolves a representational structure in response to its training data. Uniquely SCENT requires no initial parameter setting as it autonomously creates appropriate parameterisation at runtime. Pruning and convergence are stochastically controlled using locally calculated heuristics. A thorough investigation into the performance of SCENT is presented. The network is compared to other dynamic tree based models and to a high quality flat clusterer over a variety of data sets and runs

Neural Network Models of Learning and Memory: Leading Questions and an Emerging Framework

Office of Naval Research and the Defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (NIH 20-316-4304-5