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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Quasi-global oppositional fuzzy thresholding

Quasi-global oppositional fuzzy thresholding

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Hamid R. Tizhoosh Farhang Sahba Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering University of Waterloo Waterloo ONT Canada University of Toronto Toronto ONT Canada

Opposition-based computing is the paradigm for incorporating entities along with their opposites within the search, optimization and learning mechanisms. In this work, we introduce the notion of "opposite fuzzy sets" in order to use the entropy difference between a fuzzy set and its opposite to carry out object discrimination in digital images. A quasi-global scheme is used to execute the calculations, which can be employed by any other existing thresholding technique. Results for prostate ultrasound images have been provided to verify the performance whereas expert's markings have been used as gold standard.

关键词： Fuzzy sets Image segmentation Fuzzy systems machine intelligence Ultrasonic imaging Neural networks pattern analysis Laboratories System analysis and design design engineering

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Improving gradient-based learning algorithms for large scale feedforward networks

Improving gradient-based learning algorithms for large scale...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Ventresca H. R. Tizhoosh Faculty member of the Pattern Analysis and Machine Intelligence laboratory and Opposition-Based Learning Groups in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Large scale neural networks have many hundreds or thousands of parameters (weights and biases) to learn, and as a result tend to have very long training times. Small scale networks can be trained quickly by using second-order information, but these fail for large architectures due to high computational cost. Other approaches employ local search strategies, which also add to the computational cost. In this paper we present a simple method, based on opposite transfer functions which greatly improve the convergence rate and accuracy of gradient-based learning algorithms. We use two variants of the backpropagation algorithm and common benchmark data to highlight the improvements. We find statistically significant improvements in both convergence speed and accuracy.

关键词： Large-scale systems Transfer functions Neural networks Feedforward neural networks Computer architecture Computational efficiency Convergence Backpropagation algorithms Computer networks design engineering

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Particle swarm clustering ensemble 08

Particle swarm clustering ensemble

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10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008

作者： Ahmadi, Abbas Karray, Fakhri Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab. Department of Systems Design Engineering University of Waterloo 200 University Avenue West Waterloo ON Canada

ISBN: (纸本)9781605581309

Extracting natural groups of the unlabeled data is known as clustering. To improve the stability and robustness of the clustering outputs, clustering ensembles have emerged recently. In this paper, an ensemble of particle swarm clustering algorithms is proposed. That is, the members of the ensemble are based on the cooperative swarms clustering approaches. The performance of the proposed particle swarm clustering ensemble is evaluated using different data sets and is compared to that of other clustering techniques.

关键词： Particle swarm optimization (PSO)

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Numerical condition of feedforward networks with opposite transfer functions

Numerical condition of feedforward networks with opposite tr...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mario Ventresca Hamid Reza Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfer functions on the conditioning of feedforward neural network architectures. The goal is not to discuss a new training algorithm nor error surface geometry, but rather to present characteristics of opposite transfer functions which can be useful for improving existing or to develop new algorithms. Our investigation examines two situations: (1) network initialization, and (2) early stages of the learning process. We provide theoretical motivation for the consideration of opposite transfer functions as a means to improve conditioning during these situations. These theoretical results are validated by experiments on a subset of common benchmark problems. Our results also reveal the potential for opposite transfer functions in other areas of, and related to neural networks.

关键词： Transfer functions Neurons Artificial neural networks Jacobian matrices Training Feedforward neural networks Erbium

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Multiple Cooperating Swarms for Data Clustering

Multiple Cooperating Swarms for Data Clustering

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IEEE Swarm intelligence Symposium, SIS

作者： Abbas Ahmadi Fakhri Karray Mohamed Kamel Systems Design Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada Electrical and Computer Engineering Department Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada

A new clustering technique by the use of multiple swarms is proposed. The proposed technique mimics the behavior of biological swarms which explore food situated in several places. We model the clustering problem using particle swarm optimization (PSO) approach. The proposed method considers multiple cooperating swarms to find centers of clusters. By assigning a portion of the solution space to each swarm, the exploration ability to find the solution is enhanced. Moreover, the cooperation among swarms increases the between-class distance. The proposed method outperforms k-means clustering as well as conventional PSO-based clustering techniques

关键词： Particle swarm optimization Data engineering design engineering Biology computing Birds Educational institutions Marine animals Space exploration Equations pattern analysis

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Simulated Annealing with Opposite Neighbors

Simulated Annealing with Opposite Neighbors

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IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

This paper presents an improvement to the vanilla version of the simulated annealing algorithm by using opposite neighbors. This new technique, is based on the recently proposed idea of opposition based learning, as such our proposed algorithm is termed opposition-based simulated annealing (OSA). In this paper we provide a theoretical basis for the algorithm as well as its practical implementation. In order to examine the efficacy of the approach we compare the new algorithm to SA on six common real optimization problems. Our findings confirm the theoretical predictions as well as show a significant improvement in accuracy and convergence rate over traditional SA. We also provide experimental evidence for the use of opposite neighbors over purely random ones

关键词： Simulated annealing Convergence Computational intelligence Computational modeling pattern analysis Intelligent networks Competitive intelligence Laboratories System analysis and design design engineering

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Opposite Transfer Functions and Backpropagation Through Time

Opposite Transfer Functions and Backpropagation Through Time

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IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

关键词： Transfer functions Backpropagation algorithms Signal processing algorithms Recurrent neural networks Acceleration Convergence Computational intelligence Stability Nonlinear dynamical systems Computational complexity

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Opposition-Based Q(λ) with Non-Markovian Update

Opposition-Based Q(λ) with Non-Markovian Update

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, (ADPRL)

作者： Maryam Shokri Hamid R. Tizhoosh Mohamed S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo ONT Canada Department of Electrical and Computer Engineering University of Waterloo ONT Canada

The OQ(λ) algorithm benefits from an extension of eligibility traces introduced as opposition trace. This new technique is a combination of the idea of opposition and eligibility traces to deal with large state space problems in reinforcement learning applications. In our previous works the comparison of the results of OQ(λ) and conventional Watkins' Q(λ) reflected a remarkable increase in performance for the OQ(λ) algorithm. However the Markovian update of opposition traces is an issue which is investigated in this paper. It has been assumed that the opposite state can be presented to the agent. This may limit the usability of the technique to deterministic environments. In order to relax this assumption the non-Markovian opposition-based Q(λ) (NOQ(λ)) is introduced in this work. The new method is a hybrid of Markovian update for eligibility traces and non-Markovian-based update for opposition traces. The experimental results show improvements of learning speed for the proposed technique compared to Q(λ) and OQ(λ). The new technique performs faster than OQ(λ) algorithm with the same success rate and can be employed for broader range of applications since it does not require determining state transition

关键词： Learning Usability Dynamic programming pattern analysis machine intelligence Laboratories System analysis and design design engineering systems engineering and theory State-space methods

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Search Difficulty of Two-Connected Ring-based Topological Network designs

Search Difficulty of Two-Connected Ring-based Topological Ne...

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IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Beatrice Ombuki-Berman Mario Ventresca Computer Science Department Brock University Saint Catharines ONT Canada Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Ring-based network design problems have many important applications, especially in the fields of logistics and telecommunications. This paper focuses on the recently proposed two-connected network with bounded rings problem. We investigate the search difficulty of both the Euclidean edge and unit edge length ring size bounds flavors of this problem by performing an information-theoretic fitness landscape analysis for several instances of the problem. Our results further confirm the hypothesis that the unit edge length version of the problem is indeed more difficult. The investigation also further reveals that smaller sized ring bounds lead to harder problems for Euclidean edge lengths. However, for unit edge lengths we did not establish such a relationship

关键词： Logistics Performance analysis Telecommunication network topology Costs Telecommunication traffic Computational intelligence Information analysis Helium Abstracts design optimization

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