Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning

2014 ◽  
Vol 125 ◽  
pp. 166-171 ◽  
Author(s):  
Haibin Duan ◽  
Linzhi Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Planning ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm ◽  
Global Path Planning ◽  
Artificial Neural

Structural Damage Detection Using FRF Data, 2D-PCA, Artificial Neural Networks and Imperialist Competitive Algorithm Simultaneously

2017 ◽  
Vol 17 (07) ◽  
pp. 1750073 ◽  
Author(s):  
Faramarz Khoshnoudian ◽  
Saeid Talaei ◽  
Milad Fallahian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Damage ◽  
Damage Identification ◽  
Damage Index ◽  
Imperialist Competitive Algorithm ◽  
Damage Scenarios ◽  
Competitive Algorithm ◽  
Artificial Neural ◽  
Unknown Damage

In this study, a promising pattern recognition based approach is introduced for structural damage identification using the measured dynamic data. The frequency response function (FRF) is preferably employed as the input of the proposed algorithm since it contains the most information of structural dynamic characteristics. The 2D principal component analysis (2D-PCA) is used to reduce the large size of FRFs data. The output data generated by the 2D-PCA are used to extract the damage indexes for each of the damage scenarios. A dataset of all probable damage indexes is provided; of which 30% are selected to form the train dataset and to be compared with the unknown damage index for an unidentified state of the structure. The sum of absolute errors (SAE) are calculated between the unknown damage index and the selected indexes from the dataset; of which the minimum refers to the most similar damage condition to the unknown one. The artificial neural networks (ANNs) are used to form a smooth function of the SAEs and the imperialist competitive algorithm (ICA) is utilized to minimize this function in order to find the location and severity of the damages of the unknown state of the structure. To validate the proposed method, the damage identification of a truss bridge structure and a two-story frame structure is conducted by considering all the single damage cases as well as multi damage scenarios. In addition, the robustness of the proposed method to measurement noise up to 20% is thoroughly investigated.

scholarly journals Early Detection of Excess Nitrogen Consumption in Cucumber Plants Using Hyperspectral Imaging Based on Hybrid Neural Networks and the Imperialist Competitive Algorithm

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 575
Author(s):  
Sajad Sabzi ◽  
Razieh Pourdarbani ◽  
Mohammad Hossein Rohban ◽  
Ginés García-Mateos ◽  
Jitendra Paliwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Detection ◽  
Hyperspectral Imaging ◽  
Imaging Technique ◽  
Imperialist Competitive Algorithm ◽  
Nutritional Deficiencies ◽  
Excess Nitrogen ◽  
Competitive Algorithm ◽  
Artificial Neural

To achieve healthy and optimal yields of agricultural products, the principles of nutrition must be observed and appropriate fertilizers must be applied. Nutritional deficiencies or overabundance reduce the quality and yield of the products. Thus, their early detection prevents physiological disorders and associated diseases. Most research efforts have focused on spectroscopy, which extracts only spectral data from a single point of the product. The present study aims to detect early excess nitrogen in cucumber plants by using a new hyperspectral imaging technique based on a hybrid of artificial neural networks and the imperialist competitive algorithm (ANN-ICA), which can provide spectral and spatial information on the leaves at the same time. First, cucumber seeds were planted in 18 pots. The same inputs were applied to all the pots until the plants grew; after that, 30% excess nitrogen was applied to nine pots with irrigation water, while it remained constant in the other nine pots. Each day, six leaves were collected from each pot, and their images were captured using a hyperspectral camera (in the range of 400–1100 nm). The wavelengths of 715, 783 and 821 nm were determined as the most effective for early detection of excess nitrogen using a hybrid of artificial neural networks and the artificial bee colony algorithm (ANN-ABC). The parameter of days of treatment was classified using ANN-ICA. The performance of the classifier was evaluated using different criteria, namely recall, accuracy, specificity, precision and the F-measure. The results indicate that the differences between different days were statistically significant. This means that the hyperspectral imaging technique was able to detect plants with excess nitrogen in the near-infrared range (NIR), with a correct classification rate of 96.11%.

scholarly journals Characterization of a Mobile Waste-Robot: a Heuristic Method to Path Planning using Artificial Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3902-3910
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Planning ◽  
Mobile Robotics ◽  
Heuristic Method ◽  
Correlation Coefficients ◽  
Path Tracking ◽  
Ann Model ◽  
Artificial Neural ◽  
Tracking Time

In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

scholarly journals Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1653 ◽  
Author(s):  
Amir Hossein Salimi ◽  
Jafar Masoompour Samakosh ◽  
Ehsan Sharifi ◽  
Mohammad Reza Hassanvand ◽  
Amir Noori ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heavy Precipitation ◽  
Imperialist Competitive Algorithm ◽  
Absolute Error ◽  
Precipitation Data ◽  
Ann Model ◽  
Satellite Precipitation ◽  
Artificial Neural ◽  
Residual Correction

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.

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