Comparing performances of backpropagation and genetic algorithms in the data classification

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Hence, this classical training technique has many drawbacks like stuck in the local minima and maximum number of iterations required. Particle Swam Optimization (PSO) has been widely applied for the solutions of data classification problems. Population initialization is a vital factor in PSO algorithm, which considerably influences the diversity and convergence during the PSO's process. In this paper, the training of the ANN has been implemented with new initialization technique by using low discrepancies sequence, Torus termed as TO-PSO. In this paper, a detailed comparative performance analysis for the training of neural network is observed on nine benchmark data sets taken from UCI repository. The Results demonstrate that training of ANN with proposed initialization technique offer efficient and best substitute to traditional training approaches of the NN, which gives the solution of problems related to the data classification. Furthermore, the performance of TO-PSO has been compared with back propagation algorithm (BPA), standard PSO-NN and two other initialization approaches Sobol based PSO (SO-PSONN) and Halton based PSO (H-PSONN) for the training of ANN. The experimental results show that the proposed approach outperforms than BPA, traditional PSONN, SO-PSONN and H-PSONN in terms of converging speed and better accuracy Moreover, the outcomes of our work present a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

The purpose of the research, the results of which are presented in this article, is to determine the possibility and evaluate the effectiveness of using a trained neural network in the diagnosis of ringworm. The article provides an analysis of the methods used for diagnosing dermatomycosis in veterinary practice. One of the actively developing areas at present is the use of artificial neural networks in the diagnosis of animal diseases. The authors have developed a method for diagnosing dermatophytosis using a trained neural network. To identify hair damaged by dermatophyte spores in cats, a trained artificial neural network YOLO v5 was used, based on the YOLO architecture (high-precision artificial neural network), which provides high accuracy and speed of object detection in images. Diagnostics was carried out in three stages. The first stage: the diagnosis of hair in cats damaged by dermatophyte spores was carried out using a trained artificial neural network. The second stage: microscopy by a veterinary specialist of the veterinary center. The third stage: comparison of the received data from the trained artificial neural network and veterinary specialists. Three comparative experiments were carried out on 20 depersonalized samples with different ratios from healthy and sick animals. As a result of testing the trichoscopy method using artificial neural networks for diagnosing spore-damaged hair dermatitis in cats, it was found that a trained artificial neural network of 60 studied samples diagnosed dermatophyte spore damage in 20 samples, a veterinarian - in 17. All positive results were confirmed by a mycological laboratory study. and identification of the pathogen. It has been established that the use of a trained artificial neural network increases the diagnostic efficiency by 15% and reduces the time to perform diagnostic microscopy by 60.3%. The application of the proposed method allows to reduce the time of microscopic examination, improve the accuracy of interpretation of the results, automate methods for identifying causative agents of ringworm in small animals and take timely measures to treat the animal.

The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery and the artificial neural network (ANN) model constructed by these meteorological parameters and can be applied on distributed rainfall estimation. Because it is difficult to decide the structure of back propagation neural network (BPNN) and to solve the problem of local convergence, an appropriate training and modeling method of ANN such as the real code genetic algorithm (RGA) is vital to the accuracy of rainfall estimation. The data of the simulation tests show that the Mean Relative Error (MRE) of BPA model is 23.6%, while the MRE of RGA model is 20.7%, Compared with the ANN trained by BPA, the estimation error of the ANN trained by RGA is cut down by 2.9%, and the Root Mean Squared Error (RMSE) is cut down by 2.5% at the same time, hence, the results prove that the ANN model trained using RGA will significantly outperform the back propagation algorithm (BPA) trained ANN model and improve the precision of rainfall estimation. Keywords: remote sensing; EOS/MODIS; artificial neural network (ANN); back propagation algorithm (BPA); genetic algorithm (GA); distributed rainfall estimation 1. INTRODUCTION Rainfall precipitation is an important but highly variable atmospheric parameter, and in a large river basin, different area has different weather condition, conventional methods of retrieved meteorological parameters are pretty difficult to satisfy the hydrological need. While the technology of remote sensing can obtain the distributed meteorological parameters in each unit area of the river basin, therefore, remote sensing is more effective and convenient than conventional methods in relevant surveys and studies. Moreover, the existing rainfall station network cannot provide the temporal and spatial coverage which are necessary for sufficient monitoring, so their application for accurate precipitation estimation with good temporal and spatial coverage is hampered by the existing technical limitation problems. Compared with the existing rainfall station network, the satellite measurements have the advantage of providing spatially and temporally homogeneous observations over a large area, such as GMS, TM, AVHRR and MODIS satellite images. In these satellite sensors, the moderate resolution imaging spectroradiometer (MODIS) has the wide spectral range and spatial coverage of 36 spectral bands sampling the electromagnetic spectrum from 0.4 to 14 um with a spatial resolution ranging from 250 to 1,000 meters

Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Since this classical training technique has many drawbacks like stuck in the local minima, maximum number of iterations required, in this paper the training of the NN has been implemented with the opposition based with particle swarm optimization neural network (OPSONN) algorithm. These algorithms that are used for the NN training can be applied for the solutions of data classification problems. It is renowned that different techniques comparison is also as vital as by proposing a new technique for data classification. In this paper, a detailed comparative performance analysis for the training of neural network is observed on the different data sets taken from UCI repository. Results demonstrates that opposition based particle swarm optimization neural network (OPSONN) may offer efficient and best substitute to traditional training approach of the neural network for the solution of problems of data classification. The results are compared with OPSONN learning algorithm for feed forward neural network (FNN). The subsequent exactness of FNNs trained with PSO (PSONN), back propagation algorithm (BPA), and back propagation algorithm with momentum is likewise examined. The trial results demonstrate that OPSONN outperforms PSONN, back propagation algorithm (BPA), and back propagation algorithm with momentum for preparing FFNNs as far as accuracy rate and better precision. It is likewise demonstrated that an FFNN prepared with OPSONN technique has preferable exactness over one trained with different methods.

Reliable classification using neural networks: a genetic algorithm and backpropagation comparison

A comparative analysis of training methods for artificial neural network rainfall–runoff models

The backpropagation (BP) algorithm is a one of the most common algorithms used in the training of neural networks. The single offspring technique (SOFT algorithm) is a new technique (see Likartsis, A. et al., Proc. 9th Int. Conf. on Tools with Artificial Intelligence, p.32-6, 1997; Yao, X., Proc. IEEE, vol.87, p.1425-47, 1999) of applying the genetic algorithm in the training of neural networks which reduces the training time as compared with the backpropagation algorithm. We introduce a new technique. This technique is a hybrid SOFT-BP algorithm where the SOFT-algorithm is applied first to obtain an initially good weight vector. This vector is introduced to the backpropagation algorithm, which improves the precession of the weight vector to reach an acceptable error limit. The results show an acceptable improvement in the training speed for the hybrid technique as compared with the individual backpropagation or SOFT algorithm. We also study the success ratio (how many times the algorithm succeeds in finding a solution to the total number of trials) for the new hybrid algorithm. A recommended range of the switching error limit at which to switch from the SOFT algorithm to the BP algorithm is suggested.

Artificial Neural Networks design and training algorithms are based many times on the optimization of an objective error function used to provide an evaluation of the performances of the network. The value of the error depends basically on the weight values of the different connections between the neurons of the network. The learning methods modify and update the different weight values following a strategy that tends to minimize the final error in the network performance. The neural network theory identifies the weight values as a representation of the synaptic weights in the biological neural networks, and their ability to change their values can be interpreted as a kind of artificial plasticity inspired by the demonstrated biological counterpart process. The biological metaplasticity is related to the processes of memory and learning as an inherent property of the biological neuron connections, and consists in the capacity of modifying the learning mechanism using the information present in the network itself. In such a way, Artificial MetaPlasticity (AMP), is interpreted as the ability to change the efficiency of artificial plasticity depending on certain elements used in the training. A very efficient AMP model (as a function of learning time and performance) is the approach that connects metaplasticity and Shannon’s information theory, which establishes that less frequent patterns carry more information than frequent patterns. This model defines AMP as a learning procedure that produces greater modifications in the synaptic weights when less frequent patterns are presented to the network than when frequent patterns are used, as a way of extracting more information from the former than from the latter. In this doctoral thesis the AMP theory is implemented using different Artificial Neural Network (ANN), models and different learning paradigms. The networks are used as classifiers or predictors of synthetic and real data sets in order to be able to compare and evaluate the results obtained with several state of the art methods. The AMP theory is implemented over two general learning methods: • Supervised training: The BackPropagation Algorithm (BPA), is one of the best known and most used algorithms to training the neural networks. This algorithm compares the ideal results with the real results obtained at the networks output and calculates an error value. This value is used to modify the weight values in order to get a final trained network that minimizes the differences between the ideal and the real results. The BPA has been successfully applied to several patter classification problems in areas such as: medicine, bioinformatic, banking, climatological predictions, etc. However the classic algorithm has shown some limitations that prevent this method to reach an optimal efficiency level (convergence, speed problems and classification accuracy). Artificial Metaplasticity modification to the classic BPA, is in this case implemented in a Multilayer Perceptron (MLP), neural network. The Artificial Metaplasticity on MultiLayer Perceptron (AMMLP) model was applied in the ANNs training phase. During the training phase the AMMLP algorithm updates the weights assigning higher values to the less frequent activations than to the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The suggested AMMLP algorithm was applied to different problems related to pattern classification or prediction in different areas and considering different methods for obtaining the information from the data set. Modeling this interpretation in the training phase, the hypothesis of an improved training shows a much more efficient training maintaining the ANN performance. This algorithm has achieved deeper learning on several multidisciplinary data sets without the need of a deep network. • Unsupervised training: Koniocortex-Like Network (KLN) is a novel category of bio-inspired neural networks whose architecture and properties are inspired in the biological koniocortex, the first layer of the cortex that receives information from the thalamus. In the KLN competition and pattern classification emerges naturally due to the interplay of inhibitory inter-neurons, metaplasticity and intrinsic plasticity. This behavior resembles a Winner Take All (WTA) mode of operation, where the most active neuron “wins”, i.e. fires, while neighboring ones remain silent. Although a winning neuron is identified by calculation in many artificial neural networks models, in biological neural networks the winning neuron emerges from a natural dynamic process. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the first time, its competitive results are proved in several relevant real applications. The simulations show that the unsupervised learning that emerges from individual neurons properties is comparable and even surpasses results obtained with several advanced state-of-the-art supervised and unsupervised learning algorithms.

Logarithm-approximate floating-point multiplier is applicable to power-efficient neural network training

Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identifying the weight of neurons which is one of the main issues addressed in this field. The process of network learning by back-propagation algorithm which is based on gradient, commonly fall into a local optimum. Due to the importance of weights and neural network structure, evolutionary neural networks have been emerged to obtain suitable weight set. This paper will concentrate on training a feed-forward networks by a modified evolutionary algorithm based on asexual reproduction optimization (ARO) in order to data classification problems. The idea is to use real representation (rather the binary) for adjusting weights of the network. Experimental results show a better result in terms of speed and accuracy compared with other evolutionary algorithms including genetic algorithms, simulated annealing and particle swarm optimization.

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constuctivism, an alphabetization method proposed by Emilia Ferreiro (1985) based on Piaget philosophy. Simulation results show that the proposed configuration usually obtained a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

A review on neural networks with random weights

BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7]. AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality. MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks. Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses. RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms. CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.

In a bid to predict the propagation loss of electromagnetic signals, different models based on empirical and deterministic formulas have been used. In this study, different artificial neural network models which are very effective for prediction were used for the prediction of signal power loss in a microcell environment, Obio-Akpor, Port Harcourt, Nigeria. The signal power loss of the area is studied based on three artificial neural network algorithms with nine training functions. For the training of the artificial neural network, the input data were the distance from the transmitter and the signal power loss. Training of neural network is a demanding task in the field of supervised learning research. This is because the main difficulty in adopting artificial neural network is in finding the most suitable combination of learning and training functions for the prediction task. We compared the performance of three training algorithms in feedforward back propagation multi layer perceptron neural network. Nine training functions under three training algorithms were selected: the Gradient descent based algorithms, the Conjugate gradient based algorithms and the Quasi-Newton based algorithms. The work compared the training algorithms on the basis of mean square error, mean absolute error, standard deviation, correlation coefficient, regression on training and validation and the rate of convergence. The general performance of the training functions demonstrates their effectiveness to yield accurate results in short time. The conclusion on the training functions is based on the simulation results using measurement data from the micro environment.