CINTIA
CINTIA is a Neuro-Fuzzy real-time controller based on Pulse
Stream computation techniques and designed for applications in low power
embedded systems.
The proposed system mixes two different approaches, namely Neuro-Fuzzy
Controllers and Finite State Automata. The former are implemented by
means of a custom neural chip (manufactured by ES2), while the latter
are implemented as sequential code on a traditional microcontroller.
The proposed system is used to demonstrate the
advantages of mixing the two approaches and the feasibility of embedded
Neuro-Fuzzy control systems.
Real time control of non-linear systems is often a quite complex and
computationally intensive task. It is for this reason that
Neural Networks (NN) and Fuzzy Systems (FS) are gaining
widespread acceptance in the field of learning and intelligent
control. This is due mainly to their intrinsic
parallelism, their learning and adaptation capabilities and,
to some extent, also to their increased fault tolerance.
The old version of the CINTIA board.
Although NN and FS provide interesting performance
for non-linear controllers, they alone cannot satisfy all the
requirements of a real plant. In fact there are several cases where an
analog controller alone is not sufficient as, for instance, when
the plant has also ON/OFF input/outputs, or when the plant has a
number of well defined discrete states
(which cannot be easily handled by NNs and FSs)
and plant characteristics differ largely from state to state, etc.
As examples, we can mention the forward and backward steps of autonomous
walking machines, the control of electric engines running at different
speeds in different phases of an industrial process, the optimal trajectory
control of a manipulator with two largely different load conditions, and
many others.
In all these cases a more comprehensive approach is required, where Finite
State Automata (FSA) and Intelligent Controllers (IC) must
coexist and they must be tightly integrated with each other.
In such a hybrid approach, the FSA keeps track of the discrete states
of the plant and varies the controller
parameters accordingly, while the internal continuous states of the plant
may also act to switch the FSA from one state to another.
The proposed system is centered around a
full custom CMOS neural ASIC
designed using Coherent Pulse Width Modulation
(CPWM), a PS technique which shows very good performance.
Applying CPWM to Neuro-Fuzzy systems simplifies the design of synapses
and neurons and provides evident advantages with respect to either
fully analog or fully digital implementations, such
as:
high noise immunity, ease of multiplexing, low energy requirements,
straightforward interfaces with analog environments, higher accuracy,
easier reconfigurability.
The new version of the CINTIA board.
For more information look at
the article
presented at MICRONEURO 1994, The Fourth International Conference on
Microelectronics for Neural Networks and Fuzzy Systems held in
Turin -
Italy ,
26-28 September '94.
If you need a program able to handle GZIP files under Windows 95,
download
WINPACK32.
Marcello Chiaberge
<marcello@polimage.polito.it> |
Last Updated:
10/03/1997 |
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