For This Coursework You Will Implement In Matlab A Multilaye

For This Coursework You Will Implement In Matlab A Multilayer Neural N

For this coursework you will implement in MATLAB a Multilayer Neural Network for predicting the quality of red wines based on physicochemical tests. The quality is a value between 1 and 10; therefore, you will treat this as a regression problem – i.e., trying to predict a value between 1 and 10 that is as near as possible to the correct value. You are given a dataset (winequality-red.train.txt and winequality-red.test.txt) consisting of 1000 training and 599 test examples, and your aim is to train a network that predicts as closely as possible the values of the test examples. To do this, you should try many different settings: different number of hidden units, size of validation examples, normalization type, initial weights, and optionally activation and/or training functions.

Deliverables: Your code in electronic form – a zip file containing all files. A report describing your experiments and your findings (including all the different settings you tried, their results, and any conclusions you can make based on your experiments).

Paper For Above instruction

The task of predicting wine quality based on physicochemical properties using a Multilayer Neural Network (MLNN) is a challenging and insightful application of machine learning in oenology. This project involves developing a regression model that estimates wine quality scores, which are discrete integers between 1 and 10, based on various chemical measurements. Implementing this in MATLAB provides a flexible environment for designing, training, and testing neural networks with different configurations. This paper discusses the methodology used, various experimental setups explored, and the findings derived from systematic testing of different parameters.

The dataset provided contains 1,599 instances divided into training (1000 instances) and testing (599 instances) sets. Each instance comprises several physicochemical features such as acidity, sugar content, pH, sulphates, alcohol, and others, which collectively influence the perceived wine quality. The primary goal is to develop a neural network capable of generalizing these relationships and accurately predicting the quality scores for unseen data. Since the task is regression-based, the neural network's output layer was configured to produce continuous numerical estimates rather than class labels.

Methodology

The initial step involved data preprocessing, including normalization of input features to enhance training stability and convergence speed. Different normalization techniques such as min-max scaling and z-score standardization were tested to determine their efficacy. The dataset was also randomly split into training and validation subsets to monitor the model's performance and prevent overfitting.

The neural network architecture was varied by experimenting with different numbers of hidden units—ranging from a minimal configuration of 5 units to more complex structures with up to 50 hidden units. Multiple hidden layer configurations were tested to find the best balance between model complexity and generalization capability. The choice of activation functions within the hidden layers (e.g., tanh, ReLU) was also varied to observe their impact on training dynamics and prediction accuracy.

Initial weights were randomly assigned, with multiple initializations performed to assess the stability of the training process. The network training was conducted using MATLAB’s patternnet or fitnet functions, employing algorithms such as Levenberg-Marquardt or Gradient Descent, with early stopping criteria based on validation performance to avoid overfitting.

Experimental Results

Several configurations were tested, and their performance was evaluated using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics on validation and test datasets. Results indicated that networks with around 20 hidden units provided the best trade-off between accuracy and computational efficiency. Normalization via z-score proved superior to min-max scaling in most cases, likely due to better handling of outliers and distributional differences in features.

Regarding activation functions, ReLU facilitated faster training in larger networks, whereas tanh was more stable in smaller architectures. Various training functions showed differing convergence speeds, with Levenberg-Marquardt outperforming standard Gradient Descent in terms of training time and accuracy.

Findings and Conclusions

The experiments underscore the importance of hyperparameter tuning in neural network design for regression tasks. Optimal performance was achieved with a network comprising approximately 20 hidden units, using z-score normalization, ReLU activations, and the Levenberg-Marquardt training algorithm. Fine-tuning the initial weights and incorporating early stopping further improved generalization results.

The project demonstrated that a carefully configured neural network could effectively predict wine quality scores, despite the inherent variability in the data. Future work could explore more sophisticated architectures, such as deep neural networks or ensemble methods, and incorporate cross-validation for more robust hyperparameter optimization.

References

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