Abstract This paper describes how to solve numerical equations of hydraulic problems that involve... more Abstract This paper describes how to solve numerical equations of hydraulic problems that involve the calculation of free and forced channels. The problem is modeled by using the Manning equation. This equation allows the calculation of outflows, inclination of the ...
Convolutional neural networks have been attracted great attention in the realm of complex tasks, ... more Convolutional neural networks have been attracted great attention in the realm of complex tasks, mainly in image recognition. They were specifically designed to process images as inputs, as they act in local receptive fields, performing a convolution process. However, understanding the work principle of this network may not be an easy task, especially for beginners in the area of computational intelligence. Therefore, the objective of this work is to present in a didactic and intuitive way the convolutional neural networks. A case study involving alphabet character recognition is presented in order to ilustrate the feasibility of the approach.
The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden l... more The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently,with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.
Abstract: In this paper we present a new PID controllers design method for disturbance rejection.... more Abstract: In this paper we present a new PID controllers design method for disturbance rejection. The method is based on the minimization of the integral of time multiplied-squared error (ITSE) subject to a disturbance rejection constraint of the type H, norm. A design ...
Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown po... more Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown potential for solving single-objective constrained optimization problems in static environments. In this paper, a generalized BBPSO for dynamic single-objective constrained optimization problems is proposed. An empirical study was carried out to evaluate the performance of the proposed approach. Experimental results show the suitability of the proposed algorithm in terms of effectiveness to find good solutions for all benchmark problems investigated. For comparison purposes, experimental results found by other algorithms are also presented.
In this article, the use of some well-known versions of particle swarm optimization (PSO) namely ... more In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested.
... In Section V the experimental results is shown and discuss, which we compare performance ... ... more ... In Section V the experimental results is shown and discuss, which we compare performance ... multiply vi by (-1) if the particle goes beyond its boundary to search back ... the accelerated co-evolutionary PSO to solve min-max problem, two constraint optimization benchmarks from ...
Concept drift is a common problem for online sequential algorithms that deal with data streams. M... more Concept drift is a common problem for online sequential algorithms that deal with data streams. Many supervised and unsupervised approaches to solve concept drifts were proposed recently, including some ELM-based algorithms. Due to its fast training, ELM-based algorithms can quickly adapt to dataset changes, detecting and preventing concept drift. SSOE-ELM is a semi-supervised online ELM-based algorithm with good accuracy and generalization ability, but as an online algorithm it is also affected by the concept drift problem. In this paper, a variation of SSOE-ELM algorithm with a semi-supervised concept drift detector and a forgetting parameter called SSOE-FP-ELM is proposed. This new approach is compared with standard SSOE-ELM and FP-ELM. Our experimental results show that SSOE-FP-ELM outperforms SSOE-ELM and FP-ELM in accuracy with two different concept drift types, without a considerable increase in training time.
Abstract This paper describes how to solve numerical equations of hydraulic problems that involve... more Abstract This paper describes how to solve numerical equations of hydraulic problems that involve the calculation of free and forced channels. The problem is modeled by using the Manning equation. This equation allows the calculation of outflows, inclination of the ...
Convolutional neural networks have been attracted great attention in the realm of complex tasks, ... more Convolutional neural networks have been attracted great attention in the realm of complex tasks, mainly in image recognition. They were specifically designed to process images as inputs, as they act in local receptive fields, performing a convolution process. However, understanding the work principle of this network may not be an easy task, especially for beginners in the area of computational intelligence. Therefore, the objective of this work is to present in a didactic and intuitive way the convolutional neural networks. A case study involving alphabet character recognition is presented in order to ilustrate the feasibility of the approach.
The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden l... more The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently,with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.
Abstract: In this paper we present a new PID controllers design method for disturbance rejection.... more Abstract: In this paper we present a new PID controllers design method for disturbance rejection. The method is based on the minimization of the integral of time multiplied-squared error (ITSE) subject to a disturbance rejection constraint of the type H, norm. A design ...
Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown po... more Bare bones particle swarm optimization (BBPSO) is a well-known swarm algorithm which has shown potential for solving single-objective constrained optimization problems in static environments. In this paper, a generalized BBPSO for dynamic single-objective constrained optimization problems is proposed. An empirical study was carried out to evaluate the performance of the proposed approach. Experimental results show the suitability of the proposed algorithm in terms of effectiveness to find good solutions for all benchmark problems investigated. For comparison purposes, experimental results found by other algorithms are also presented.
In this article, the use of some well-known versions of particle swarm optimization (PSO) namely ... more In this article, the use of some well-known versions of particle swarm optimization (PSO) namely the canonical PSO, the bare bones PSO (BBPSO) and the fully informed particle swarm (FIPS) is investigated on multimodal optimization problems. A hybrid approach which consists of swarm algorithms combined with a jump strategy in order to escape from local optima is developed and tested.
... In Section V the experimental results is shown and discuss, which we compare performance ... ... more ... In Section V the experimental results is shown and discuss, which we compare performance ... multiply vi by (-1) if the particle goes beyond its boundary to search back ... the accelerated co-evolutionary PSO to solve min-max problem, two constraint optimization benchmarks from ...
Concept drift is a common problem for online sequential algorithms that deal with data streams. M... more Concept drift is a common problem for online sequential algorithms that deal with data streams. Many supervised and unsupervised approaches to solve concept drifts were proposed recently, including some ELM-based algorithms. Due to its fast training, ELM-based algorithms can quickly adapt to dataset changes, detecting and preventing concept drift. SSOE-ELM is a semi-supervised online ELM-based algorithm with good accuracy and generalization ability, but as an online algorithm it is also affected by the concept drift problem. In this paper, a variation of SSOE-ELM algorithm with a semi-supervised concept drift detector and a forgetting parameter called SSOE-FP-ELM is proposed. This new approach is compared with standard SSOE-ELM and FP-ELM. Our experimental results show that SSOE-FP-ELM outperforms SSOE-ELM and FP-ELM in accuracy with two different concept drift types, without a considerable increase in training time.
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Papers by Renato Krohling