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International Conference on Computational Cybernetics (ICCC)

作者： Zsolt Dányádi Krisztián Balázs László T Kóczy Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Hungary Electrical and Mechanical Engineering Széchenyi István University Györ Hungary

ISBN: (纸本)9781424474325;9781424474332

The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.

关键词： Evolutionary computation Fuzzy systems Learning systems Cloning Microorganisms inference algorithms Informatics Genetic algorithms Genomics Bioinformatics

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Multi-Criterion Phylogenetic inference using Evolutionary algorithms

Multi-Criterion Phylogenetic Inference using Evolutionary Al...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Waldo Cancino A.C.B. Delbem Institute of Mathematics and Computer Science University of Sao Paulo Sao Paulo Brazil

Various phylogenetic reconstruction methods have been proposed in order to determine the most accurate tree that represents evolutionary relationships among species. Each method defines a criterion for evaluation of possible solutions. This criterion leads the search to the best phylogenetic tree. However, different criteria may lead to distinct phylogenies, which often conflict with each other. In this context, a multi-objective approach can be useful since it could produce a set of optimal trees (Pareto front) according to multiple criteria. We propose a multi-objective evolutionary algorithm, called Phylo-MOEA, which is focused on maximum parsimony and maximum likelihood criteria. In experiments, several PhyloMOEA trials were performed using four datasets of nucleotide sequences. For each dataset, the proposed algorithm found a Pareto front representing a trade-off between the criteria used. Moreover, SH-test showed that a number of solutions from PhyloMOEA are not significantly worse than solutions found by phylogenetic programs using one criterion

关键词： Phylogeny Evolutionary computation Reconstruction algorithms Computational biology Sequences Bioinformatics inference algorithms Computational intelligence Mathematics Computer science

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Bayesian inference in linear models with a random Gaussian matrix : algorithms and complexity

Bayesian inference in linear models with a random Gaussian m...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Ido Nevat Gareth W. Peters Jinhong Yuan School of Electrical Engineering and Telecommunications University of NSW Sydney Australia School of Mathematics and Statistics University of NSW and CSIRO Mathematical and Information Sciences Sydney Australia

We consider the Bayesian inference of a random Gaussian vector in a linear model with a random Gaussian matrix. We review two approaches to finding the MAP estimator for this model. We propose improved versions of these approaches with reduced complexity. Next we analyze their complexity and convergence properties. Then we derive the MAP estimator in the setting in which the variance of the noise is unknown. Simulation results presented compare the performance in terms of estimation error of the approaches.

关键词： Bayesian methods inference algorithms Vectors Mathematical model Australia Signal processing algorithms Mathematics Statistics Convergence Estimation error

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Belief-Rule-Base inference Method Based on Gradient Descent With Momentum

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IEEE ACCESS 2021年 9卷 34487-34499页

作者： Guan, Yu Fu, Yanggeng Chen, Longjiang Liu, Genggeng Sun, Lan Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China

The belief-rule-base (BRB) inference methodology using the evidential reasoning (ER) approach is widely used in different fields, such as fault diagnosis, system identification, and decision analysis. However, the calculation characteristic of the conventional rule activation weight makes the inference system have the rule zero activation problem. The difficulty of constructing partial derivatives restricts the optimization of parameters using the gradient method. Hence, this paper proposes a new belief rule structure and its gradient training method to solve the rule zero activation problem during the inference process and improve inference accuracy. The Gaussian function is applied to calculate the activation weight of the rule with the new structure. Its characteristics avoid the zero activation problem caused by the attribute reference value set in the original method. Based on the newly proposed method, the corresponding distance-sensitive parameter is set for each attribute, and the weight parameter of each rule is discarded. It simplifies the calculation of rule activation weights in the inference process and enables the partial derivatives of the parameters of the inference system to be easily constructed. In the parameter optimization, the momentum optimization gradient stochastic descent method is used to train the new BRB system, which improves the training speed and accuracy compared with the conventional methods. Experiments with nonlinear function fitting, oil pipeline leak detection, and classification of several public datasets are carried out to verify whether the new BRB system trained with momentum stochastic gradient descent (SGDM-BRB) has better performance than other conventional methods. The experimental results show that in the case of complete data, SGDM-BRB has higher accuracy and faster training speed than the conventional methods.

关键词： Training Optimization Cognition Training data inference algorithms Stochastic processes Licenses Belief-rule-base structure optimization stochastic gradient descent momentum optimization

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GRAMMATICAL inference - INTRODUCTION AND SURVEY .1.

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1986年第3期8卷 343-359页

作者： FU, KS BOOTH, TL UNIV CONNECTICUT DEPT ELECT ENGN & COMP SCISTORRSCT 06268 PURDUE UNIV SCH ELECT ENGNW LAFAYETTEIN 47907

The problem of grammatical inference is introduced, and its potential engineering applications are demonstrated. inference algorithms for finite-state and context-free grammars are presented. The application of some of the algorithms to the inference of pattern grammars in syntactic pattern recognition is illustrated by examples.","doi":"10.1109/TPAMI.1986.4767796","publicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","startPage":"343","endPage":"359","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=Grammatical+inference%3A+Introduction+and+Survey-Part+I&isbn=&publicationDate=May+1986&author=King-Sun+Fu&ContentID=10.1109/TPAMI.1986.4767796&orderBeanReset=true&startPage=343&endPage=359&volumeNum=PAMI-8&issueNum=3","displayPublicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","pdfPath":"/iel5/34/4767784/***","keywords":[{"type":"IEEE Keywords","kwd":["inference algorithms","Pattern recognition","Computer languages","Data structures","Character generation","Learning","Design engineering","Information retrieval","Graphics","Man machine systems"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767784","standardTitle":"Grammatical inference: Introduction and Survey-Part I

关键词： inference algorithms Pattern recognition Computer languages Data structures Character generation Learning Design engineering Information retrieval Graphics Man machine systems

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An Oscillator-Synchronization-Based Off-Line Learning Algorithm, With On-Chip inference on an Array of Spin Hall Nano-Oscillators

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IEEE TRANSACTIONS ON NANOTECHNOLOGY 2023年 22卷 136-148页

作者： Bhotla, Sri Vasudha Hemadri Garg, Neha Aggarwal, Tanmay Muduli, Pranaba Kishor Bhowmik, Debanjan Indian Inst Technol Delhi Dept Phys New Delhi 110016 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India Indian Inst Technol Dept Elect Engn Mumbai 400076 India

Learning algorithms based on synchronization of oscillators are currently being pursued as an alternative to standard neural-network algorithms for data classification. For such oscillator-based algorithms, while learning can be implemented off-line on a computer, on-chip inference/ testing can be implemented on an array of uniform-mode spin Hall nano-oscillators (SHNOs), leading to a scalable and energy-efficient technology. Here, we propose a modification to an existing oscillator-based off-line learning algorithm for binary classification: unlike in the previous version of the algorithm, here the difference between natural frequencies of the output oscillators is kept constant throughout the learning process. This helps in preservation of the shape of the synchronization region and leads to higher classification accuracy, as we show for binary-classification tasks using two popular data sets: Fisher's Iris and MNIST. Next, in this paper, we show how a synchronization pattern obtained after such training, using our proposed algorithm, can be implemented on an array of dipole-coupled uniform-mode SHNOs for on-chip inference. We model the SHNO system through the macro-spin model, which is computationally much less resource-intensive to simulate compared to the micromagnetic model used previously for this kind of study. We also extend our algorithm to multi-class classification and thereby discuss scaling of our system.

关键词： Oscillators Classification algorithms inference algorithms Computational modeling Synchronization Mathematical models Training Oscillator-based computing Neuromorphic computing Spin Hall Nano-Oscillator

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Optimization of Waiting Time for Electric Vehicles Using a Fuzzy inference System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15396-15407页

作者： Hussain, Shahid Kim, Yun-Su Thakur, Subhasis Breslin, John G. Natl Univ Ireland Galway NUIG Data Sci Inst DSI Dept Elect & Elect Engn Galway H91 TK33 Ireland Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

Electric vehicles (EVs) need to be recharged at intermediate locations, such as shopping malls, restaurants, and parking lots, to meet the daily commute requirements of their users. Currently, public electric vehicle supply equipment (EVSE) serve EVs by conventional methods, which can result in long waiting time for users. This issue reduces the travel efficiency of EVs and thus affects user comfort. Most previous research has studied energy consumption and charging cost optimization;however, comparatively less work has focused on waiting time optimization despite its great importance from the EV user's perspective. In this paper, we formulate the waiting time optimization as a fuzzy integer linear programming problem and propose a novel heuristic fuzzy inference system-based algorithm (FISA) that resolves the objective function and minimizes the waiting time of EVs at public EVSE installations. We developed the underlying fuzzy inference system by defining the membership functions, expert rules, and formulation for obtaining the optimal solution. The novel FISA automates the correlations of the uncertain and independent input parameters into weighted control variables and resolves the objective function in each sampling period to optimize the waiting time for EVs with the most urgent service requirements. A java language-based simulator is developed for a parking lot to evaluate the effectiveness of the proposed FISA. The simulation results indicate higher efficiency of the proposed FISA compared with state-of-art scheduling algorithms.

关键词： Optimization Fuzzy logic Indexes Linear programming inference algorithms Costs Batteries Charging and waiting times electric vehicles electric vehicle supply equipment fuzzy integer linear programming fuzzy inference system

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Riffled Independence for Efficient inference with Partial Rankings

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2012年第1期44卷 491-532页

作者： Huang, Jonathan Kapoor, Ashish Guestrin, Carlos Stanford Univ James H Clark Ctr Stanford CA 94305 USA Microsoft Res Redmond WA 98052 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

Distributions over rankings are used to model data in a multitude of real world settings such as preference analysis and political elections. Modeling such distributions presents several computational challenges, however, due to the factorial size of the set of rankings over an item set. Some of these challenges are quite familiar to the artificial intelligence community, such as how to compactly represent a distribution over a combinatorially large space, and how to efficiently perform probabilistic inference with these representations. With respect to ranking, however, there is the additional challenge of what we refer to as human task complexity - users are rarely willing to provide a full ranking over a long list of candidates, instead often preferring to provide partial ranking information. Simultaneously addressing all of these challenges - i.e., designing a compactly representable model which is amenable to efficient inference and can be learned using partial ranking data - is a difficult task, but is necessary if we would like to scale to problems with nontrivial size. In this paper, we show that the recently proposed riffled independence assumptions cleanly and efficiently address each of the above challenges. In particular, we establish a tight mathematical connection between the concepts of riffled independence and of partial rankings. This correspondence not only allows us to then develop efficient and exact algorithms for performing inference tasks using riffled independence based representations with partial rankings, but somewhat surprisingly, also shows that efficient inference is not possible for riffle independent models (in a certain sense) with observations which do not take the form of partial rankings. Finally, using our inference algorithm, we introduce the first method for learning riffled independence based models from partially ranked data.

关键词： representations inference algorithms Bayesian inference Partial

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GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian inference

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第1期31卷 53-65页

作者： Yi, Wooseok Park, Junki Kim, Jae-Joon Pohang Univ Sci & Technol POSTECH Dept Creat IT Engn Pohang 37673 South Korea

The probabilistic Bayesian inference of real-time input data is becoming more popular, and the importance of semisupervised learning is growing. We present a classification restricted Boltzmann machine (ClassRBM)-based hardware accelerator with on-chip semisupervised learning and Bayesian inference capability. ClassRBM is a specific type of Markov network that can perform classification tasks and reconstruct its input data. ClassRBM has several advantages in terms of hardware implementation compared to other backpropagation-based neural networks. However, its accuracy is relatively low compared to backpropagation-based learning. To improve the accuracy of ClassRBM, we propose the multi-neuron-per-class (multi-NPC) voting scheme. We also reveal that the contrastive divergence (CD) algorithm, which is commonly used to train RBM, shows poor performance in this multi-NPC ClassRBM. As an alternative, we propose an asymmetric contrastive divergence (ACD) training algorithm that improves the accuracy of multi-NPC ClassRBM. With the ACD learning algorithm, ClassRBM operates in the form of a combination of Markov Chain training and Bayesian inference. The experimental results on a field-programmable gate array (FPGA) board for a Modified National Institute of Standards and Technology data set confirm that the inference accuracy of the proposed ACD algorithm is 5.82% higher for a supervised learning case and 12.78% higher for a 1% labeled semisupervised learning case than the conventional CD algorithm. Also, the GeCo ver.2 hardware implemented on a Xilinx ZCU102 FPGA board was 349.04 times faster than the C simulation on CPU.

关键词： Hardware Training Neurons System-on-chip inference algorithms Semisupervised learning Bayes methods Bayesian inference classification restricted Boltzmann machine (ClassRBM) generative core (GeCo) generative model hardware accelerator neural network on-chip learning restricted Boltzmann machine (RBM) semisupervised

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Improving Effectiveness of Simulation-Based inference in the Massively Parallel Regime

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第4期34卷 1100-1114页

作者： Kulkarni, Sourabh Moritz, Csaba Andras Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

Simulation-based inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in High-Performance Compute clusters, these algorithms have been shown to scale in performance when developed to be run on massively parallel architectures such as GPUs. While parallelizing existing SBI algorithms provides us with performance gains, this might not be the most efficient way to utilize the achieved parallelism. This work proposes a new parallelism-aware adaptation of an existing SBI method, namely approximate Bayesian computation with Sequential Monte Carlo(ABC-SMC). This new adaptation is designed to utilize the parallelism not only for performance gain, but also toward qualitative benefits in the learnt parameters. The key idea is to replace the notion of a single 'step-size' hyperparameter, which governs how the state space of parameters is explored during learning, with step-sizes sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm to more efficiently explore the state-space of the parameters being learned. We test the effectiveness of the proposed algorithm to learn parameters for an epidemiology model running on a Tesla T4 GPU. Compared to the parallelized state-of-the-art SBI algorithm, we get similar quality results in similar to 100x fewer simulations and observe similar to 80x lower run-to-run variance across 10 independent trials.

关键词： inference algorithms Computational modeling Biological system modeling Approximation algorithms Perturbation methods Optimization Bayes methods Approximate Bayesian computation Bayesian inference COVID-19 compartmental models epidemiology GPU high performance computing likelihood-free inference parallel algorithms simulation-based inference statistical machine learning stochastic optimization

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