摘要
随着人工智能技术的快速发展，其在电子系统设计中的应用逐渐成为研究热点。本研究旨在探索基于人工智能的电子系统优化设计方法，以应对传统设计方法在复杂性和效率方面的局限性。研究首先分析了当前电子系统设计面临的挑战，包括日益增长的性能需求和资源约束之间的矛盾，以及传统算法在处理高维非线性问题时的不足。为解决这些问题，本文提出了一种融合深度学习与强化学习的混合优化框架，该框架能够自动提取电子系统的特征并生成高效的优化策略。通过构建多目标优化模型，结合遗传算法与神经网络预测能力，实现了对电子系统性能、功耗和可靠性的综合优化。实验结果表明，所提出的优化方法在多个典型电子系统设计任务中表现出显著优势，不仅大幅提升了设计效率，还有效降低了系统能耗和硬件成本。此外，本研究创新性地引入了自适应学习机制，使优化过程能够根据实际应用场景动态调整参数，从而增强了方法的普适性和鲁棒性。综上所述，本研究为人工智能驱动的电子系统优化设计提供了新的思路，并为未来相关领域的研究奠定了理论和技术基础。
关键词：人工智能；电子系统优化；深度学习
Abstract
With the rapid development of artificial intelligence technologies, their applications in electronic system design have gradually become a research hotspot. This study aims to explore AI-based optimization design methods for electronic systems to address the limitations of traditional approaches in terms of complexity and efficiency. It begins by analyzing the challenges currently faced in electronic system design, including the growing contradiction between performance requirements and resource constraints, as well as the inadequacies of conventional algorithms in handling high-dimensional nonlinear problems. To tackle these issues, this paper proposes a hybrid optimization fr amework that integrates deep learning and reinforcement learning, enabling automatic feature extraction of electronic systems and the generation of efficient optimization strategies. By constructing a multi-ob jective optimization model that combines genetic algorithms with the predictive capabilities of neural networks, comprehensive optimization of electronic system performance, power consumption, and reliability is achieved. Experimental results demonstrate that the proposed optimization method exhibits significant advantages in multiple typical electronic system design tasks, not only substantially improving design efficiency but also effectively reducing system energy consumption and hardware costs. Furthermore, this study innovatively introduces an adaptive learning mechanism, allowing the optimization process to dynamically adjust parameters according to real-world application scenarios, thereby enhancing the versatility and robustness of the method. In summary, this research provides new insights into AI-driven optimization design for electronic systems and lays a theoretical and technical foundation for future studies in related fields.
Keywords：Artificial Intelligence; Electronic System Optimization; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、人工智能在电子系统优化中的基础理论 2
(一) 人工智能技术的核心原理 2
(二) 电子系统优化的基本概念 3
(三) 人工智能与电子系统优化的结合点 3
(四) 常见优化算法及其应用 4
三、基于人工智能的电子系统性能优化设计 5
(一) 性能优化的关键指标分析 5
(二) 深度学习在性能优化中的应用 5
(三) 强化学习对系统性能的提升作用 6
(四) 实验验证与结果分析 6
四、基于人工智能的电子系统能耗优化设计 7
(一) 能耗优化的研究框架 7
(二) 数据驱动的能耗建模方法 7
(三) 智能算法在能耗优化中的实现 8
(四) 典型案例分析与评估 9
结 论 10
参考文献 11
随着人工智能技术的快速发展，其在电子系统设计中的应用逐渐成为研究热点。本研究旨在探索基于人工智能的电子系统优化设计方法，以应对传统设计方法在复杂性和效率方面的局限性。研究首先分析了当前电子系统设计面临的挑战，包括日益增长的性能需求和资源约束之间的矛盾，以及传统算法在处理高维非线性问题时的不足。为解决这些问题，本文提出了一种融合深度学习与强化学习的混合优化框架，该框架能够自动提取电子系统的特征并生成高效的优化策略。通过构建多目标优化模型，结合遗传算法与神经网络预测能力，实现了对电子系统性能、功耗和可靠性的综合优化。实验结果表明，所提出的优化方法在多个典型电子系统设计任务中表现出显著优势，不仅大幅提升了设计效率，还有效降低了系统能耗和硬件成本。此外，本研究创新性地引入了自适应学习机制，使优化过程能够根据实际应用场景动态调整参数，从而增强了方法的普适性和鲁棒性。综上所述，本研究为人工智能驱动的电子系统优化设计提供了新的思路，并为未来相关领域的研究奠定了理论和技术基础。
关键词：人工智能；电子系统优化；深度学习
Abstract
With the rapid development of artificial intelligence technologies, their applications in electronic system design have gradually become a research hotspot. This study aims to explore AI-based optimization design methods for electronic systems to address the limitations of traditional approaches in terms of complexity and efficiency. It begins by analyzing the challenges currently faced in electronic system design, including the growing contradiction between performance requirements and resource constraints, as well as the inadequacies of conventional algorithms in handling high-dimensional nonlinear problems. To tackle these issues, this paper proposes a hybrid optimization fr amework that integrates deep learning and reinforcement learning, enabling automatic feature extraction of electronic systems and the generation of efficient optimization strategies. By constructing a multi-ob jective optimization model that combines genetic algorithms with the predictive capabilities of neural networks, comprehensive optimization of electronic system performance, power consumption, and reliability is achieved. Experimental results demonstrate that the proposed optimization method exhibits significant advantages in multiple typical electronic system design tasks, not only substantially improving design efficiency but also effectively reducing system energy consumption and hardware costs. Furthermore, this study innovatively introduces an adaptive learning mechanism, allowing the optimization process to dynamically adjust parameters according to real-world application scenarios, thereby enhancing the versatility and robustness of the method. In summary, this research provides new insights into AI-driven optimization design for electronic systems and lays a theoretical and technical foundation for future studies in related fields.
Keywords：Artificial Intelligence; Electronic System Optimization; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、人工智能在电子系统优化中的基础理论 2
(一) 人工智能技术的核心原理 2
(二) 电子系统优化的基本概念 3
(三) 人工智能与电子系统优化的结合点 3
(四) 常见优化算法及其应用 4
三、基于人工智能的电子系统性能优化设计 5
(一) 性能优化的关键指标分析 5
(二) 深度学习在性能优化中的应用 5
(三) 强化学习对系统性能的提升作用 6
(四) 实验验证与结果分析 6
四、基于人工智能的电子系统能耗优化设计 7
(一) 能耗优化的研究框架 7
(二) 数据驱动的能耗建模方法 7
(三) 智能算法在能耗优化中的实现 8
(四) 典型案例分析与评估 9
结 论 10
参考文献 11
