scholarly journals GENERALIZED CELLULAR NEURAL NETWORKS (GCNNs) CONSTRUCTED USING PARTICLE SWARM OPTIMIZATION FOR SPATIO-TEMPORAL EVOLUTIONARY PATTERN IDENTIFICATION

2008 ◽  
Vol 18 (12) ◽  
pp. 3611-3624 ◽  
Author(s):  
H. L. WEI ◽  
S. A. BILLINGS
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Orthogonal Projection ◽  
Learning Algorithm ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Cellular Neural Networks ◽  
Modeling Framework ◽  
Swarm Optimization ◽  
Spatio Temporal

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to refine and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem.

Training oscillatory neural networks using natural gradient particle swarm optimization

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1551-1567 ◽  
Author(s):  
Hamed Shahbazi ◽  
Kamal Jamshidi ◽  
Amir Hasan Monadjemi ◽  
Hafez Eslami Manoochehri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Humanoid Robots ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Oscillatory Neural Network

SUMMARYIn this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

Particle Swarm Optimization Technique for Task-Resource Scheduling for Robotic Clouds

2014 ◽  
Vol 565 ◽  
pp. 243-246 ◽  
Author(s):  
Vladimir Popov
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Scheduling Problem ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Gradient Learning

The task-resource scheduling problem is one of the fundamental problems for cloud computing. There are a large number of heuristics based approaches to various scheduling workflow applications. In this paper, we consider the problem for robotic clouds. We propose new method of selection of parameters of a particle swarm optimization algorithm for solution of the task-resource scheduling problem for robotic clouds. In particular, for the prediction of values of the inertia weight we consider genetic algorithms, multilayer perceptron networks with gradient learning algorithm, recurrent neural networks with gradient learning algorithm, and 4-order Runge Kutta neural networks with different learning algorithms. Also, we present experimental results for different intelligent algorithms.

A Prediction Modeling Framework: Toward Integration of Noisy Manufacturing Data and Product Design

2015 ◽  
Author(s):  
Junheung Park ◽  
Kyoung-Yun Kim ◽  
Raj Sohmshetty
Keyword(s):  
Particle Swarm Optimization ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Particle Swarm ◽  
Prediction Modeling ◽  
Modeling Framework ◽  
Swarm Optimization ◽  
Meta Modeling

In many design and manufacturing applications, data inconsistency or noise is common. These data can be used to create opportunities and/or support critical decisions in many applications, for example, welding quality prediction for material selection and quality monitoring applications. Typical approaches to deal with these data issues are to remove or alter them before constructing any model or conducting any analysis to draw decisions. However, these approaches are limited especially when each data carries important value to extract additional information about the nature of the given problem. In the literature, with the presence of noise in data, bootstrap aggregating has shown an improvement in the prediction accuracy. In order to achieve such an improvement, a bagging model has to be carefully constructed. The base learning algorithm, number of base learning algorithms, and parameters for the base learning algorithms are crucial design parameters in that aspect. Evolutionary algorithms such as genetic algorithm and particle swarm optimization have shown promising results in determining good parameters for different learning algorithms such as multilayer perceptron neural network and support vector regression. However, the computational cost of an evolutionary computation algorithm is usually high as they require a large number of candidate solution evaluations. This requirement even more increases when bagging is involved rather than a single learning algorithm. To reduce such high computational cost, a metamodeling approach is introduced to particle swarm optimization. The meta-modeling approach reduces the number of fitness function evaluations in the particle swarm optimization process and therefore the overall computational cost can be reduced. In this paper, we propose a prediction modeling framework whose aim is to construct a bagging model to improve the prediction accuracy on noisy data. The proposed framework is tested on an artificially generated noisy dataset. The quality of final solutions obtained by the proposed framework is reasonable compared to particle swarm optimization without meta-modeling. In addition, using the proposed framework, the largest improvement in the computational time is about 42 percent.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

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