DESIGN OF A DEVS-BASED ANN TRAINING AND PREDICTION PLATFORM

DESIGN OF A DEVS-BASED ANN TRAINING AND PREDICTION PLATFORM

Abstract:

Artificial Neural Networks (ANNs) have emerged as powerful tools for solving complex problems in various domains such as image recognition, natural language processing, and financial prediction. However, training and deploying ANNs efficiently and effectively remains a challenge. This abstract presents the design of a DEVS (Discrete Event System Specification)-based platform for ANN training and prediction.

The proposed platform leverages the DEVS formalism, which provides a solid foundation for modeling and simulating complex systems. By integrating DEVS with ANNs, the platform enables the modeling and simulation of the training process, allowing for more efficient optimization and fine-tuning of network architectures and hyperparameters.

The design of the platform includes several key components. Firstly, a DEVS-based modeling framework is established, which captures the dynamics of the ANN training process. This framework allows for the representation of network layers, activation functions, and weight updates as discrete events, facilitating the simulation and analysis of the training process.

Secondly, the platform incorporates a scalable and distributed computing infrastructure to handle the computational demands of ANN training. By leveraging parallel processing and distributed computing techniques, the platform can efficiently train large-scale ANNs by harnessing the power of modern hardware architectures.

Furthermore, the platform provides an intuitive user interface and visualization tools for monitoring and analyzing the training and prediction processes. Users can track the training progress, visualize the network performance, and explore the impact of different configurations on prediction accuracy.

The proposed DEVS-based platform offers several advantages over existing ANN training and prediction frameworks. By providing a simulation environment, it enables researchers and practitioners to gain deeper insights into the training process and better understand the behavior of ANNs. Additionally, the platform’s distributed computing capabilities facilitate the training of large-scale ANNs, leading to improved performance and scalability.

In conclusion, the design of a DEVS-based ANN training and prediction platform offers an innovative approach to address the challenges associated with ANN training and deployment. By leveraging the DEVS formalism and integrating it with ANNs, the platform provides a powerful tool for optimizing network architectures, improving prediction accuracy, and gaining insights into the training process. Future work will involve implementing and evaluating the proposed platform to demonstrate its effectiveness in real-world scenarios.

DESIGN OF A DEVS-BASED ANN TRAINING AND PREDICTION PLATFORM. GET MORE  COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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