32,755 research outputs found
Adaptive Resonance Theory
SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
Neuro-fuzzy chip to handle complex tasks with analog performance
This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input–output delay, and precision, performs as a fully analog implementation.
However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting
of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core.
Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture
are smaller than those of its purely analog counterparts simply because most rules are implemented through programming.
The Paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype.
This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 um standard technology. It has
two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided
Neuro-fuzzy chip to handle complex tasks with analog performance
This Paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of
power consumption, input-output delay and precision performs as a fully analog
implementation. However, it has much larger complexity than its purely analog
counterparts. This combination of performance and complexity is achieved through
the use of a mixed-signal architecture consisting of a programmable analog core of
reduced complexity, and a strategy, and the associated mixed-signal circuitry, to
cover the whole input space through the dynamic programming of this core [1].
Since errors and delays are proportional to the reduced number of fuzzy rules
included in the analog core, they are much smaller than in the case where the whole
rule set is implemented by analog circuitry. Also, the area and the power
consumption of the new architecture are smaller than those of its purely analog
counterparts simply because most rules are implemented through programming.
The Paper presents a set of building blocks associated to this architecture, and gives
results for an exemplary prototype. This prototype, called MFCON, has been
realized in a CMOS 0.7μm standard technology. It has two inputs, implements 64
rules and features 500ns of input to output delay with 16mW of power consumption.
Results from the chip in a control application with a DC motor are also provided
Recommended from our members
Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
No abstract availabl
- …