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11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Neuro-fuzzy chip to handle complex tasks with analog performance.
R de Jesus Navas-Gonzalez1, F Vidal-Verdu, A Rodriguez-Vazquez
1Dept. of Electron., Malaga Univ., Spain.
IEEE Transactions on Neural Networks
|February 5, 2008
Summary
A novel mixed-signal neuro-fuzzy controller chip offers analog-level performance with reduced complexity and power consumption. This innovative design dynamically programs a core, achieving high precision and speed for control applications.
Area of Science:
- Mixed-signal processing
- Neuro-fuzzy systems
- Embedded control systems
Background:
- Traditional analog fuzzy controllers face limitations in complexity and scalability.
- Purely digital implementations often struggle with real-time processing demands and power efficiency.
- A need exists for intelligent control solutions balancing performance, complexity, and resource utilization.
Purpose of the Study:
- To introduce a mixed-signal neuro-fuzzy controller chip with enhanced performance and reduced complexity.
- To demonstrate a novel architecture that dynamically programs a core to cover the input space.
- To present a prototype chip (MFCON) and evaluate its performance in a control application.
Main Methods:
- Development of a mixed-signal architecture combining a programmable analog core with dynamic programming strategies.
- Implementation of a reduced-complexity analog core, with fuzzy rules programmed dynamically to cover the input space.
- Realization of a prototype chip (MFCON) using CMOS 0.7 μm technology, featuring two inputs and 64 rules.
Main Results:
- The mixed-signal controller achieves performance comparable to fully analog implementations in terms of power consumption, I/O delay, and precision.
- Errors and delays are reduced due to the smaller number of fuzzy rules in the analog core compared to full analog implementations.
- The prototype chip (MFCON) exhibits a 500 ns input-to-output delay and 16-mW power consumption.
- The architecture demonstrates smaller area and power consumption than purely analog counterparts due to rule programming.
Conclusions:
- The proposed mixed-signal neuro-fuzzy controller architecture offers a compelling balance of performance, complexity, and efficiency.
- Dynamic programming of a reduced analog core is an effective strategy for implementing complex fuzzy logic systems.
- The MFCON prototype successfully controls a DC motor, validating the chip's practical applicability in embedded control systems.
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