Design and Training for Combinational Neural-Logic Systems (original) (raw)

Artificial Neural Network Application in Logic System

IJCA Proceedings on …, 2012

The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in controlsystems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The ...

Combinational Logical Circuits Simulation Using Feed Forward Neural Networks

2007

ABSTRACT. The combinational logic circuit (CLC) is an important chapter in the project activity of the electronic equipments. If the number of variables is greater than three the project activity with Veitch-Karnaugh diagrams become very difficult. On the other side, the feedforward artificial neural networks have several characteristics like noise immunity, fault tolerance etc. that can be advantages for logical circuits made by these networks. In this papers we presents a new method in simulation of the combinational logic circuit with the neuronal network. There are two important advantages that we can remark: the number of transistors is reduced and the design will become simpler.

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Neural Computing and Applications, 2007

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.

An encoding technique for design and optimization of combinational logic circuit

Proceedings of 13th International Conference on Computer and Information Technology (ICCIT 2010), 2010

A neural representation of combinational logic circuit is proposed, called ‘Logical Neural Network’ (LNN). LNN is a feed-forward neural network (NN) where the weights of the network indicate the connections of digital circuit. The logic operations of the circuit such as AND, OR, NOR etc are performed with the neurons of LNN. A modification of Simple Genetic Algorithm (mSGA) is applied to design and optimize the LNN for a given truth table. The proposed technique is experimentally studied on four bit parity checker, two bit multiplexer, two bit full adder, full subtractor, and two bit multiplier circuits. LNN is compared with conventional ‘Cell Array’ method. LNN outperforms the Cell Array method in terms of number of required gates.

Multilayer Backpropagation Neural Networks for Implementation of Logic Gates

2021

ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained by using a backpropagation algorithm until the model satisfies the predefined error criteria (e) which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively.

Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic

This paper describes the design and development of a prototype technique for artificial intelligence based on the fusion of genetic algorithm, neural network and fuzzy logic. It starts by establishing a relationship between the neural network and fuzzy logic. Then, it combines the genetic algorithm with them. Information fusions are at the confidence level, where matching scores can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy (GNF). It can be used for high accuracy real-time environments.

Design of Various Logic Gates in Neural Networks

IEEE, 2013

This work presents a CMOS technique for designing and implementing a biologically inspired neuron which will accept multiple synaptic inputs. The circuit accepts synapses as inputs and generates a pulse width modulated output waveform of constant frequency depending on the level of activation. Next, the behavior of this implementation has been presented, and the realization of various basic logic gates through this combination has been realized.

Design and Implementation of Logic Gates using Artificial Neural Networks on FPGA

2017

In this paper, a hardware implementation of artificial neural networks and implementation of logic gates using artificial neural networks on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture for feed forward multilayer neural network is realized. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks that makes a neural network well suited for implementation in VLSI technology. Then logic gates are implemented using Feed Forward Neural Network. FPGA has been used to reduce the unit neuron hardware by designing the activation function inside the neuron without the need of lookup tables. The whole design is realized using Verilog HDL and is implemented on FPGA.

A Neural Network Performing Boolean Logic Operations

2003

A neural network, composed of neurons of two types, able to perform Boolean operations is presented. Based on recursive definition of ”basic Boolean operations” the proposed model, for any fixed number of input variables, can either realize all of them paralelly, or accomplish any chosen one of them alone. Moreover, possibilities of combining a few such networks into more complex structures in order to perform superpositions of basic operations are disscussed. The general concept of neural implementation of First Order logic (FO) based on the presented network is also introduced.