Journal of Intelligent & Fuzzy Systems - Volume 47, issue 3-4

Authors: Sun, Junfeng | Li, Chenghai | Song, Yafei

Article Type: Research Article

Abstract: Network security situation prediction is a complex task that typically requires extensive retraining of deep-learning models on vast amounts of sample data to achieve optimal performance. This paper proposes an innovative approach that integrates Model-Agnostic Meta-Learning (MAML) with Bidirectional Gated Recurrent Units (BiGRU) to address these challenges. Our method harnesses the BiGRU model’s capability to learn from both preceding and succeeding conditions within network security prediction data, effectively extracting temporal information essential for prediction. This is complemented by Stochastic Gradient Descent for parameter updates, enhancing the model’s adaptability and learning efficiency. Furthermore, the MAML algorithm is incorporated to facilitate the …BiGRU model’s swift adaptation to new tasks, thereby improving its generalization capabilities. The parameters are refined through a meta-learning process that calculates the sum of losses across multiple training instances and employs quadratic gradient descent for optimization. The empirical results of our approach demonstrate significant advancements, with goodness-of-fit decision coefficients of 0.926983 and 0.934452, representing a marked improvement of at least 18.0% and 15.8% over conventional deep learning models in the domain of network security situation prediction. This research novelty lies in the synergistic combination of MAML and BiGRU, which not only reduces the dependency on large datasets for retraining but also enhances the model’s predictive accuracy and generalization to novel network security scenarios. It contributes a robust and efficient solution to the critical problem of network security situation prediction and paves the way for future advancements in cybersecurity defense mechanisms. Show more

Keywords: Network security, situation prediction, meta-learning, neural networks, small samples

DOI: 10.3233/JIFS-241801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 3-4, pp. 307-319, 2024

Authors: Sun, Changzhi | Li, Tai | Wu, Mingxi | Chu, Shiwei

Article Type: Research Article

Abstract: The circuit structure optimizationed with the traditional method is often difficult to meet the complex and changeable design requirements. In this paper the A3C algorithm has been applied to integrate strategy learning and value learning for the circuit structure optimization. This integration can facilitate continuous interaction with the environment, enabling automatic adjustment of circuit structures to meet the complex design requirements. Gain, bandwidth, latency, and power consumption have been set as the optimization objectives, and the actions of the intelligent agent, which include adding, deleting, modifying connection lines, and adjusting component parameters have been introduced in detail. Once the Actor …and Critic networks have been established, multiple agents can operate concurrently, translating optimization objectives into reward signals and providing direction and motivation for agent learning. Then with the proposed method, the circuit structure of one switch audio power amplifier has been designed in a simulation environment. The structure optimization results demonstrated that the gain can reach 78.4 dB at convergence of the A3C algorithm, while the bandwidth can reach 156.2 MHz at convergence, and both the circuit delay and power consumption have been reduced significantly. Obviously, the application of the A3C algorithm can effectively optimize the circuit structures through offering more flexible and efficient solutions. Show more

Keywords: Circuit structure, adaptive optimization, electronic design, deep reinforcement learning, Asynchronous Advantage Actor-Critic

DOI: 10.3233/JIFS-241935

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 3-4, pp. 321-332, 2024

Article Type: Retraction

DOI: 10.3233/JIFS-219432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 3-4, pp. 333-333, 2024