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Analysis of Using Binary and Bipolar Data in Knowing the Logic Gate Using Perceptron Method International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878,Volume-7, Issue-6S5, April 2019 Analysis of Using Binary and Bipolar Data in Knowing the Logic Gate Using Perceptron Method J Simangunsong, S Efendi , P H Putra Abstract: Is a logic gate circuit tha forms the basis of the II. RELATED RESEARCH computer. Millions of transistors in a microprocessor forming Ravichandran.K, et al. In this study, using a multilayer thousands of logic gates. Some methods or models are frequently perceptron to be able to understand the impact of crime. used to recognize the logic gate is a method perceptron. This gap influence on people being analyzed using Perceptron is a fast and reliable network for a class of problems multilayer perceptron [2]. that can be solved. While the propagation is one of the Artificial Neural Network architecture that has a learning process forward Qingbin Wang, et al. In this study, using digital image and backward error correction. In this study, the authors analyze processing technology to process images and then extract. the use of binary data and recognizing bipolar logic gate using This study sample group 420 enter into multilayer perceptron. From the results of this study concluded, among perceptron [3]. others: Methods perceptron can recognize the logic gate much Mohri, Mehryar, et al, In the study explained that the faster than other methods. Binary better use of the bipolar method perceptron can recognize patterns of training, but numbers, as the binary number can recognize the logic gate there are some limits to existing errors in the perceptron much faster than bipolar numbers. OR and NOR logic gates with algorithm and propose a new method that can be used to binary input and output more quickly recognized than other lower limits in the setting generalize stochastic [4]. logic gates with a maximum error of 0.1 and the number of iterations 20. Index terms:Binary, Bipolar, Logical Gates, Perceptron. III. PROPOSED METHOD A.Logic Gates I. INTRODUCTION Logic gate is a circuit with one or more input signals, but only produce a signal in the form of high voltage or low Is a logic gate circuit that forms the basis of the computer or voltage as the output signal. Logic gates are the basis of sneentity in electronics and Boolean math that changing digital system formation. Logic gate used include OR, one or more input logic into a logic output signal. AND, NOR, NAND, Ex-OR, and Ex-NOR in which [1]: Especially logic gate implemented electronically using diode or transistor, But can also be built using the a. OR gate arrangement of components that utilize the properties OR gate is a basic logic circuit which states that its electromagnetic(Relay),fluid, optics and even mechanics, output will have a logic 1 if one of its inputs have a Millions of transistors in a microprocessor forming logic 1 or everything has a logic 1. thousands of logic gates. A simple logic gate has one input terminal. The output can be high / high (1) or low / low (0), b. AND gate depending on the digital level given to the input terminal AND gate is a basic logic circuit which states its output [1]. will have a logic 1 if all its inputs logic 1. Many methods can be used to identify data patterns such as c. NOR gate logic gates. Some methods or models are frequently used to NOR gates as the OR gate, but the output is the inverter recognize the logic gate is a method perceptron. (reverse). Perceptron is a fast and reliable network for a class of d. NAND gate problems that can be solved. In addition, understanding of NAND gate as the gate, but the output is the inverter the operation of the perceptron provides a good foundation (reverse). for understanding the more complex networks [1]. e. Ex-OR gate The author assumes that not all the data on the pattern of Ex-OR is an OR gate and exclusive because its output logic gates can be recognized well by methods perceptron. will be 0 if the input is the same and its output will be The author will use binary data, bipolar, and worth 1 if any one of its inputs valued differently. hybrid (binary input and output bipolar). The purpose of f. Ex-NOR gate this study was to analyze the comparison perceptron Ex-NOR gate is a gate Ex-OR, written inverter so that method, which method can recognize patterns of data the truth table simply by reversing the Ex-OR truth provided is better, and which method can recognize data table. patterns with the smallest number of iterations. Revised Manuscript Received on December 22, 2018. J SimangunsongStudent, Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia S EfendiDepartment of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia P H PutraStudent, Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia Published By: Retrieval Number:F12370476S519/19©BEIESP 1362 Blue Eyes Intelligence Engineering 1362 & Sciences Publication Analysis of Using Binary and Bipolar Data in Knowing the Logic Gate Using Perceptron Method B.Perceptron Perceptron network method is the best model at the time. Data This model was found by Rosenblatt (1962) and Minsky - Target Data Papert (1969). Perceptron training algorithm is as follows input Ex- Ex- [5]: X1 X2 OR AND NOR NAND a. Initialize all weights and biases (usually = 0). OR NOR Set learning rate. , For simplification set equal to 1.∝ 0 0 0 0 1 1 0 1 (0 < ∝ ≤ 1) 1 0 1 0 0 1 1 0 Set the threshold value (for the activation function.휃) 0 1 1 0 0 1 1 0 b. For each pair of st learning, working on: 1 1 1 1 0 0 0 1 1) Set activation input unit, indicated by equation (1). The pattern of (1)the data in Table 1 (a) is a data pattern with two input data and the target data that varies with the 푥푖 = 푠푖; 2) calculated response to the output unit, as indicated maximum learning rate = 0.7 epoch = 150000. by equation (2). The chart comparison squared error reduction method and bakpropagation perceptron on NOR logic functions 푦푖푛 = 푏 + ∑푖 푥푖푤푖 3) Put it in the activation function, indicated by with a target error of(2) 0.1 is shown in Figure 1. equation (3). perceptron 1, 푗푖푘푎 푦푖푛 > 휃 푦 = { 0, 푗푖푘푎 − 휃 ≤ 푦푖푛 ≤ 휃 (3) −1, 푗푖푘푎 푦푖푛 < − 휃 4) Compare the value of the network output y with the target t. if y t make any changes to the weights and biases, as indicated by equation (4) and equation (5). 푤 (푏푎푟푢) = 푤 (푙푎푚푎) + 훼∗푡∗푥 푖 푖 푖 (4) ( ) ( ) ∗ 푏 푏푎푟푢 = 푏 푙푎푚푎 + 훼 푡 (5) Figure 1. Decrease graph Squares Error if y = t, no change in the weights and biases, as indicated by equation (6) and equation (7). In the test data binary inputs and the target binary 푤푖(푏푎푟푢) = 푤푖(푙푎푚푎) method perceptron one who can recognize patterns of data 푏(푏푎푟푢) = 푏(푙푎푚푎) faster than backpropagation, but the method of propagation c. Perform continuous iteration until all patterns can recognize all patterns of data provided by either did not have the same output with the target tissue. That is like the method perceptron that can not recognize patterns when all output equal to the target tissue so the of data supplied to the logic function ex-OR and ex-NOR. network has to recognize patterns well and Next is to test the pattern shape of the input data to the iteration stopped. target bipolar bipolar shown in Table 2. Perceptron training algorithm is used both for binary inputs or bipolar, with particular, and bias can be set. A Table 2 (a). Data Input Bipolar Bipolar with Target Data training cycle that involves all the input data is called an epoch. Data IV.RESEARCH METHODOLOGY input Target Data The purpose of this study was to analyze the comparison Ex- Ex- method perceptron. To achieve these objectives, the author X1 X2 OR AND NOR NAND OR NOR will conduct training (training) in the data patterns of logic -1 -1 -1 -1 1 1 -1 1 gates. The data used is the binary input data to the target 1 -1 1 -1 -1 1 1 -1 binary, bipolar input data with bipolar targets, and binary input data with bipolar targets (hybrid). -1 1 1 -1 -1 1 1 -1 Data to be trained is data with two inputs and one 1 1 1 1 -1 -1 -1 1 target. Pattern recognition is done by adjusting the weights. Termination weighting adjustments in the pattern recognition when squared error reaches the target than the specified error. The pattern of the data in Table 2 (a) is a data pattern with two input data and the target data that varies with the learning rate and the same maximum epoch previous data IV. RESULTS AND DISCUSSIONS testing, the maximum learning rate = 0.7 epoch = 150000. Training and testing data by comparing the perceptron Next is to test the form with the data pattern with a method using three different data patterns and each targets target bipolar binary inputs (hybrid) shown in Table 3. different. The shape of the pattern of the first data to be trained and tested are shown in Table 1 with the binary input data and target data binary. Table 1 (a). Binary Input Data with Data Target Binary Published By: Retrieval Number:F12370476S519/19©BEIESP 1363 Blue Eyes Intelligence 1363 Engineering & Sciences Publication International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878,Volume-7, Issue-6S5, April 2019 Table 3 (a).
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