基于反复加深的模糊启发式搜索算法及其学习性质研究

本文基于反复加深和动态修改启发式估价函数这一机制。提出了模糊启发式搜索算法ＦＩＤＡ和Ｉｍｐｒｏｖｅｄ－ＦＩＤＡ。针对模糊启发式估价函数通常难以设计这一问题，提出了可用于模糊启发式估价函数学习的学习算法ＬＦＩＤＡ。     

从"统计"到"理解",从"传输"到"认知"

今年是Shannon信息论问世的50周年．为了纪念这一伟大事件，本文回顾了它的杰出成就及其划时代贡献，也指出了这一理论不可避免的时代局限；着重评述了自Shannon信息论问世以来信息科学在世界范围内的主要进步，特别强调了这一领域的巨大变革和质的飞跃──从统计信息理论到全信息理论，从信息传输到信息认知，从通信理论到智能科学．作者认为，人们应当在科学上作好充分的准备，去迎接信息时代的新阶段──智能科学时代的到来．     

集成电容设计的逆向神经网络建模

结合人工神经网络和电磁仿真,给出了一种用于综合交指电容及Metal-Insulator-Metal(MIM)电容结构参数的方法。基于逆向神经网络,可有效地根据给定频点上的电容值快速准确地综合出其对应的结构参数,从而避免了反复优化的过程。同时,可以由训练好的神经网络参数得到结构参数相对于等效电容的闭式计算公式。数值结果验证了方法的正确性和有效性。     

神经网络模型在多模多馈天线设计中的应用

文章采用人工神经网络对多模多馈天线进行建模,并应用于多模多馈天线的分析与设计中。由于神经网络具有精度高,实时调用速度快等优点,因此文章建立的多模多馈天线神经网络设计方法具有准确、可靠、省时及其它辅助设计等优点。文中的仿真结果证明了该方法在多模多馈天线分析设计中的有效性。     

First principles versus artificial neural network modelling of a solar desalination system with experimental validation

ABSTRACT

The present study mainly focuses on enhancing the performance of solar still unit using solar energy through cylindrical parabolic collector and solar panels. A 300 W solar panel is used to heat saline water by thermal elements outside the solar still unit. Solar panels are cooled during the hot hours of the day; thus, reducing their temperature may lead to an increase in solar panel efficiency followed by an increase in the efficiency of the solar still unit. The maximum amount of freshwater used in the experiment was 2.132 kg/day. The experiments were modelled using ANNs. Based on neural network simulation results, there is a significant correlation between experimental data and neural network modelling. This paper compares experimental data with data obtained from mathematical modelling and ANNs. As a conclusion, the artificial neural network prediction has been more accurate than the simplified first principles model presented.     

Improving weighted information criterion by using optimization

Although artificial neural networks (ANN) have been widely used in forecasting time series, the determination of the best model is still a problem that has been studied a lot. Various approaches available in the literature have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use a model selection strategy based on the weighted information criterion (WIC). WIC is calculated by summing weighted different selection criteria which measure the forecasting accuracy of an ANN model in different ways. In the calculation of WIC, the weights of different selection criteria are determined heuristically. In this study, these weights are calculated by using optimization in order to obtain a more consistent criterion. Four real time series are analyzed in order to show the efficiency of the improved WIC. When the weights are determined based on the optimization, it is obviously seen that the improved WIC produces better results.     

Limitations of learning in automata-based systems

In this article, we aim to analyze the limitations of learning in automata-based systems by introducing the L⁺$L^{+}$ algorithm to replicate quasi-perfect learning, i.e., a situation in which the learner can get the correct answer to any of his queries. This extreme assumption allows the generalization of any limitations of the learning algorithm to less sophisticated learning systems. We analyze the conditions under which the L⁺$L^{+}$ infers the correct automaton and when it fails to do so. In the context of the repeated prisoners’ dilemma, we exemplify how the L⁺$L^{+}$ may fail to learn the correct automaton. We prove that a sufficient condition for the L⁺$L^{+}$ algorithm to learn the correct automaton is to use a large number of look-ahead steps. Finally, we show empirically, in the product differentiation problem, that the computational time of the L⁺$L^{+}$ algorithm is polynomial on the number of states but exponential on the number of agents.     

精度与程度的逻辑或近似算子的性质

本文目的是探讨精度与程度的复合,探索新的粗糙集拓展模型.从精度与程度的逻辑或运算出发,定义了精度与程度的逻辑或粗糙集模型.在该模型中,通过变精度近似与程度近似的转化公式,研究了精度与程度的逻辑或近似算子,并得到了该近似算子的幂作用等性质.用精度与程度的逻辑或粗糙集模型统一了变精度粗糙集模型、程度粗糙集模型和经典粗糙集模型,并在这些粗糙集模型中得到了近似算子幂作用的相应性质.     

Data Envelopment Analysis of multiple response experiments

Taguchi method is the usual strategy in robust design and involves conducting experiments using orthogonal arrays and estimating the combination of factor levels that optimizes a given performance measure, typically a signal-to-noise ratio. The problem is more complex in the case of multiple responses since the combinations of factor levels that optimize the different responses usually differ. In this paper, an Artificial Neural Network, trained with the experiments results, is used to estimate the responses for all factor level combinations. After that, Data Envelopment Analysis (DEA) is used first to select the efficient (i.e. non-dominated) factor level combinations and then for choosing among them the one which leads to a most robust quality loss penalization. Mean Square Deviations of the quality characteristics are used as DEA inputs. Among the advantages of the proposed approach over traditional Taguchi method are the non-parametric, non-linear way of estimating quality loss measures for unobserved factor combinations and the non-parametric character of the performance evaluation of all the factor combinations. The proposed approach is applied to a number of case studies from the literature and compared with existing approaches.     

Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.