Application of the McCulloch-Pitts Method Using Python for Testing Pattern Recognition of AND Operators, OR Operators, XOR Operators in Logic Functions

Abstract

Artificial neural networks (ANNs) are a part of artificial intelligence that applies the concept of how the biological neural network in humans operates. The primary advantage of ANNs lies in their ability to recognize specific patterns, such as the pattern notation of logic functions tested in this study using the McCulloch-Pitts model. Testing the pattern recognition of logic functions, involving AND, OR, and XOR operators, entails input variables: values X1, X2, and other input values (Xn), as well as target values (t) that match the truth table forms of each logic function. The test results indicate the alignment of the testing process using the concept of artificial neural networks with the patterns of logic functions. The implementation of the test is carried out using the Python programming language, which can produce conformity in the pattern forms of logic functions. The testing process is conducted by determining input values for each variable used in the logic functions, inputting weights (W) for each input value, and setting a threshold value directly to obtain results that align the output with the target.