{"id":2748,"date":"2020-03-11T09:52:01","date_gmt":"2020-03-11T09:52:01","guid":{"rendered":"https:\/\/bvcoend.ac.in\/?page_id=2748"},"modified":"2025-03-12T08:10:58","modified_gmt":"2025-03-12T08:10:58","slug":"nnfl-lab-ice","status":"publish","type":"page","link":"https:\/\/bvcoend.ac.in\/index.php\/nnfl-lab-ice\/","title":{"rendered":"NNFL Lab – ICE"},"content":{"rendered":"[vc_row][vc_column width=”1\/4″][vc_wp_custommenu title=”Imp Links” nav_menu=”69″][\/vc_column][vc_column width=”3\/4″][vc_column_text]\n

NNFL LAB<\/strong><\/span> \u00a0 \u00a0<\/span>\u00a0 \u00a0 <\/span><\/p>\n

\"\"\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0About LAB<\/strong><\/span><\/p>\n

A Neural Network and Fuzzy Logic Lab<\/strong> is designed to provide hands-on experience in artificial intelligence (AI), machine learning (ML), and computational intelligence techniques<\/strong>. This lab enables students to explore the integration of neural networks and fuzzy logic<\/strong>, which are widely used in decision-making, pattern recognition, control systems, and various engineering applications.<\/p>\n

Objectives of the Lab<\/strong><\/h3>\n

\u2022 To understand the fundamental principles of neural networks and fuzzy logic<\/strong>.
\u2022 To implement different types of artificial neural networks (ANN)<\/strong> and train them for various applications.
\u2022 To design and analyze Fuzzy Logic Controllers (FLC)<\/strong> for decision-making under uncertainty.
\u2022 To develop Hybrid Neuro-Fuzzy Systems<\/strong> that integrate the learning capability of neural networks with the reasoning power of fuzzy logic.
\u2022 To use MATLAB, Python, or other software tools<\/strong> for simulation and implementation.
\u2022 To apply neural networks and fuzzy logic in real-world applications<\/strong> such as robotics, control systems, image processing, and pattern recognition.<\/p>\n

\n

Key Components of the Lab<\/strong><\/h3>\n

1. Software Tools Used<\/strong><\/h4>\n
    \n
  • MATLAB with Neural Network & Fuzzy Logic Toolbox<\/strong><\/li>\n
  • Python (TensorFlow, Keras, PyTorch, Scikit-Fuzzy, SciPy)<\/strong><\/li>\n
  • Simulink for system modeling and simulation<\/strong><\/li>\n
  • R and OpenAI Gym for reinforcement learning applications<\/strong><\/li>\n<\/ul>\n

    2. Hardware (if used for real-time applications)<\/strong><\/h4>\n
      \n
    • Microcontrollers (Arduino, Raspberry Pi, ESP32) for fuzzy and neural-based control systems<\/strong><\/li>\n
    • Digital Signal Processing (DSP) boards for ANN-based computations<\/strong><\/li>\n
    • Sensors for real-time data acquisition in control applications<\/strong><\/li>\n<\/ul>\n

      List of Experiments:<\/strong><\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
       <\/p>\n

      Sr. No.<\/td>\n

      		Title of Lab Experiments<\/td>\nCO<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nWrite a program in MATLAB to plot triangular, trapezoidal, and bell-shaped membership functions<\/td>\nCO3<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 2.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nWrite MATLAB program for Back Propagation Neural Network Algorithm.<\/td>\nCO2<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 3.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nTo write a Program in MATLAB to perform addition, subtraction, and multiplication operations of fuzzy set.<\/td>\nCO3<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 4.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nSimulate a Fuzzy logic-based boost converter<\/td>\nCO3<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 5.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nWrite a program in MATLAB to implement AND gate using artificial neural network with Back Propagation<\/p>\n

      algorithm<\/td>\n

      		CO2<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 6.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nTo design a neural network using neural network toolbox to identify the<\/p>\n

      given data set.<\/td>\n

      		CO2<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 7.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nTo design a controller using FIS (Fuzzy Inference Editor).<\/td>\nCO4<\/td>\n<\/tr>\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 8.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/td>\nWrite a Matlab program to implement AND function using perceptron networks with bipolar inputs and outputs.<\/td>\nCO1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/strong><\/p>\n

      \u00a0<\/strong><\/p>\n

      \u00a0<\/strong><\/p>\n\n\n\n\n\n
      \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 INNOVATIVE EXPERIMENTS<\/strong><\/td>\n<\/tr>\n
      1<\/strong>.<\/td>\nDesign a fuzzy controller to calculate the washing time of washing machine.<\/td>\nCO4<\/td>\n<\/tr>\n
      2.<\/strong><\/td>\nDesign and simulate an air conditioning system using fuzzy logic control<\/td>\nCO4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/strong><\/p>\n

      \u00a0<\/strong><\/p>\n

      \u00a0<\/strong><\/p>\n

      \u00a0<\/strong><\/p>\n

      \u00a0NOTE: – At least 8 Experiments out of the list must be done in the semester.<\/strong><\/span><\/p>\n[\/vc_column_text][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

      [vc_row][vc_column width=”1\/4″][vc_wp_custommenu title=”Imp Links” nav_menu=”69″][\/vc_column][vc_column width=”3\/4″][vc_column_text] NNFL LAB \u00a0 \u00a0\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0About LAB A Neural Network and Fuzzy Logic Lab is designed to provide hands-on experience in artificial intelligence (AI), machine learning (ML), and computational intelligence techniques. This lab enables students to explore the integration of…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/2748"}],"collection":[{"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/comments?post=2748"}],"version-history":[{"count":5,"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/2748\/revisions"}],"predecessor-version":[{"id":13231,"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/pages\/2748\/revisions\/13231"}],"wp:attachment":[{"href":"https:\/\/bvcoend.ac.in\/index.php\/wp-json\/wp\/v2\/media?parent=2748"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}