Source: http://web-ext.u-aizu.ac.jp/official/researchact/annual-review/2003/hard/CLDL_j.html
Timestamp: 2019-04-24 10:41:29+00:00

Document:
In 2003, the laboratory research has been conducting in two directions: CMOS beta-Driven Threshold Elements and Artificial Neurons, and CMOS Neuro-Fuzzy Circuits and Devices.
Synapse circuits were suggested with two memory elements for storing positive and negative input weights along with the procedure of on-chip learning.
The results of Spice simulation proved that the problem of neuron teaching to non-isotonous threshold functions has been successfully solved.
The second direction is connected with the problem of designing CMOS Neuro-Fuzzy Circuits and Devices. During several last decades for solutions of sophisticated control problems and data processing effectively developed neuromorphic methods, i.e. methods inspired with knowledge of processes in a nervous system. The special place among these methods takes ANN (Artificial Neural Networks). ANN can be realized as software implementation on universal or specialized processors. Alternative to this is analog-digital hardware implementation of the ANN. The main advantage analog-digital implementation as contrasted to software implementation is the principled increase of relation throughput/complexity. The main lack of this implementation is the limitation on implementability, i.e. on complexity of functions implemented by one element. Increasing of above relation is the main result of the beta-driven circuitry application. The niche for analog digital ANN actuates: image preprocessing (artificial retina etc.), intellectual fuzzy controllers, robotic control (locomotion, scrub moving etc.), pattern recognition, fault detection, and many others. The research in this direction implies the creation of methods and tools of designing full and semi-custom neuro-fuzzy VLSI and embedded devices and systems. These methods and tools include creating threshold elements and devices, learnable beta-driven artificial neurons, fuzzy threshold elements and devices; embedded neuro-processors, neuro-arrays and fuzzy controllers and correspondent IP (intellectual properties); design methods and design know-how for analog/digital devices and systems.
[marak-01:2003] V. Varshavsky, V. Marakhovsky, I. Levin, and N. Kravchenko. Summing Amplifier as a Multi-Valued Logical Element for Fuzzy Control. WSEAS Transactions on Circuit and Systems, 2(3):625-631, 2003.
It is offered to implement fuzzy devices as multi-valued logic functions, using directly analog input variables and forming the output variables as analog ones as well. It is offered to use CMOS summing amplifiers as basic elements for designing appropriate circuits. It has been proved that a CMOS summing amplifier is a functionally complete element in arbitrary-valued logic. In a plenty of cases this approach enables principally simplification of fuzzy logic controllers for a broad class of applications. All mentioned above is illustrated by examples.
[marak-02:2003] V. Varshavsky and V. Marakhovsky. Artificial Neuron Learnable to Threshold Functions. WSEAS Transactions on Circuits and Systems, 3(1):141-148, 2003.
In the paper, we discuss a possibility of digital-analog CMOS implementation of the artificial neuron learnable to logical threshold functions. The implementation is based on earlier suggested (-driven neuron circuit consisting of synapses with excitatory inputs, (-comparator and three output amplifiers. Such a circuit can be taught only to threshold functions with positive weights of variables, which belong to the class of isotonous Boolean functions. However, most problems solved by artificial neural networks either require inhibitory inputs. It means that a neuron should have synapses capable of forming weights and types of inputs (excitatory or inhibitory) during the learning using only increment and decrement signals. The neuron with such synapses can be learned to an arbitrary threshold function of a certain number of variables. The synapse circuit with two memory elements for storing positive and negative input weights and on-chip learning algorithm are suggested. The results of SPICE simulation prove that this algorithm provides the learning process convergence independently from initial conditions.
[marak-03:2003] V. Varshavsky, V. Marakhovsky, I. Levin, and N. Kravchenko. Summing Amplifier as a Multi-Valued Logical Element for Fuzzy Control. In 3rd WSEAS Int. Conf. on Systems Theory and Scientific Computation (ISTASC'03), CD edition, page 6, Rhodes Island, Greece, Nov. 2003. WSEAS.
[marak-04:2003] V. Varshavsky and V. Marakhovsky. Artificial Neuron Learnable to Threshold Functions. In 4th WSEAS Int. Conf. on Automation and Information (ICAI'03), CDedition, page 7, Tenerife, Spain, Dec. 2003. WSEAS.
[marak-05:2003] V. Varshavsky, V. Marakhovsky, and I. Levin. Artificial Neurons Based on CMOS Beta-Driven Threshold Elements with Functional Inputs. In 5rd WSEAS Int. Conf. on Neural Networks and Applications (NNA'04), CD edition, page 6, Udine, Italy, March 2004. WSEAS.
This paper deals with a CMOS based artificial neuronimplemented by threshold elements.We consider the artificial neuron as a threshold element with controlled inputs having weights formed during a learning process. A socold beta-driven threshold element is used for in the scheme of the neuron.Functioning of this element is described in a specific ratio form. The beta-driven implementation is based on using summarized conductivities of n- and p-chains of a CMOS gate as the ratio of weighted sums. The thresh- old element has a wider functional capability in comparison with the traditional functional basis. Moreover, its functional capability can be enriched. We propose a method for increasing the functional capability of the thresh-old element by introducing so-called functional inputs.Each functional input corresponds to a boolean sum (or product) of a particular subset of input variables. This sum (or product) serves as a single input of the threshold element. It is shown that introducing functional inputs enables expansion of the functional capability of beta-driven elements up to the capability to implement an arbitrary monotonic function. The CMOS based implementation of the beta-driven threshold element with newly proposed functional inputs is presented. Methods of the current stabilization of functional inputs are proposed. In the proposed implementation of the artificial neuron, each input weight is determined by the current value via a suitable current stabilizer. This value can be effectively controlled by the value of the voltage at the gate of one of the current stabilizer's transistors. The paper presents examples of the SPICE simulation of behavior of the proposed artificial neuron in the modes of learning and maintaining the input weight values.
[marak-06:2003] V. Varshavsky, I. Levin, V. Marakhovsky, A. Ruderman, and N. Kravchenko. CMOS Fuzzy Decision Diagram Implementation. In 5rd WSEAS Int. Conf. on Fuzzy Sets and Fuzzy Systems (FSFS'04), CD edition, page 6, Udine, Italy, March 2004. WSEAS.
The subject of the study is design of multi-valued (analog) CMOS fuzzy controllers. A functional completeness of summing amplifier with saturation in a multi-valued logic of an arbitrary value proven in previous works gives a theoretical background for analog implementation of fuzzy devices. Compared with the traditional approach based on explicit fuzzification / defuzzification procedures analog fuzzy implementation has the advantages of higher speed, lower consumption, smaller die area and more. In the present paper, we expand functional capabilities of summing amplifier by using "masking of the input". The paper provides design example for an industrial fuzzy controller implementation by the proposed mask circuit and SPICE simulations of the controller.
[marak-09:2003] V. Varshavsky, V. Marakhovsky, I. Levin, and N. Kravchenko.
[marak-10:2003] V. Varshavsky, V. Marakhovsky, I. Levin, and N. Kravchenko.
Multi-Valued Logic Device, March 2004.
[marak-11:2003] Sadao Niitsuma. Graduation Thesis: Summing Amplifier as a Multi-Valued Logical Element for Fuzzy Control, University of Aizu, 2004.

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