Artificial neural networks (ANNs) are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers, which process and transmit information. Here are the basic models of ANNs and their applications:

• Consists of an input layer and an output layer.
• Each neuron in the input layer is connected to every neuron in the output layer.

• Typically uses a step function to produce binary outputs.
• Can only solve linearly separable problems.

• Binary Classification: Used in simple classification tasks where the data is linearly separable, such as spam detection or email categorization.
• Pattern Recognition: Basic tasks like recognizing specific shapes or patterns in images where linear decision boundaries are sufficient.

• Comprises multiple layers—an input layer, one or more hidden layers, and an output layer.
• Each layer's neurons are fully connected to the next layer's neurons.

• Uses non-linear activation functions like sigmoid, tanh, or ReLU.
• Utilizes backpropagation to adjust weights and minimize error.
• Can solve complex, non-linear problems.

• Speech Recognition: Converting spoken language into text.
• Image Recognition: Classifying images into categories, such as identifying handwritten digits (MNIST dataset).
• Financial Forecasting: Predicting stock prices or market trends based on historical data.
• Medical Diagnosis: Assisting in diagnosing diseases by analyzing patient data.

• Includes convolutional layers, pooling layers, and fully connected layers.

• Convolutional layers apply filters to detect spatial hierarchies.
• Pooling layers reduce dimensionality.
• Efficient at handling large-scale image data due to parameter sharing and spatial invariance.

• Image Classification: Identifying objects in images (e.g., ImageNet competition).
• Facial Recognition: Identifying or verifying a person from a digital image or video frame.
• Medical Imaging: Detecting anomalies in X-rays, MRIs, and CT scans.
• Object Detection: Identifying and locating objects within an image or video (e.g., autonomous driving).

• Neurons have connections forming directed cycles, allowing information to persist.

• Suitable for sequence data as they can maintain a memory of previous inputs.
• Variants include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to handle long-term dependencies.

• Natural Language Processing (NLP): Language modeling, machine translation, and sentiment analysis.
• Time Series Prediction: Forecasting weather, stock prices, and sales data.
• Speech Synthesis: Generating human-like speech from text (e.g., text-to-speech systems).
• Music Composition: Creating sequences of musical notes.

• Consists of an input layer, a single hidden layer with radial basis function neurons, and an output layer.

• Uses Gaussian functions as activation functions in the hidden layer.
• Training involves determining the centers and spreads of the RBF neurons and adjusting the weights to the output layer.

• Function Approximation: Interpolating functions where the relationship between variables is unknown.
• System Control: Adaptive control systems in robotics and automated systems.
• Anomaly Detection: Identifying unusual patterns in data, such as fraud detection.
• Medical Diagnosis: Classifying medical conditions based on patient data.

• A type of unsupervised learning neural network with a single layer of neurons arranged in a grid.

• Neurons compete to become the winning neuron, representing the input data pattern.
• Involves competitive learning, where neurons adapt their weights to match the input patterns.

• Data Clustering: Grouping similar data points together for exploratory data analysis.
• Dimensionality Reduction: Reducing high-dimensional data to 2D or 3D for visualization purposes.
• Market Segmentation: Identifying different customer segments based on purchasing behavior.
• Image Compression: Reducing the amount of data needed to store images by clustering similar pixel patterns.

These basic models of artificial neural networks provide a foundation for various applications, including fuzzy logic, where they can enhance decision-making processes by handling uncertainty and imprecision in data.