Hybrid Multilayered Perceptron Network Research Articles

Comparison of enhanced neural network and response surface models in predicting bio-dissolution of aluminum and vanadium from bauxite residue by isolated Aspergillus niger strains

Background: Recovery of Al and V from red mud, a hazardous residue of the Bayer process, using bioleaching is a nature-based waste management solution that simultaneously improves environment protection and metallurgical recoveries.Methods: For the first time, a novel hybrid multilayer Perceptron (MLP) network enhanced by an Imperialist Competitive Algorithm (ICA), and a response surface developed based on a factorial experiment design methodology (RSM) were employed and compared for, prediction optimization and optimization prediction of Al and V bioleaching by strains of A. Niger microorganisms isolated from pistachio shells and grape skins. The controlling variables were fungi source (A), adoption strategy (B), solid activation (C), solid percent (D), and bioleaching time. Considering the stochastic nature of ICA, a multi-criteria-ranking system based on accuracy and error indices was developed to select the best MLP. Probability value at 95% confidence interval, lack-of-fit, analysis of variance, R2, Adj. R2, and predicted R2 were the judges for the determination of the developed model's statistical significance.Significant Findings: Based on ANOVA, between A, B, C, and D, the effectiveness orders of A > D > C>AC>AD on Al, and A>AD>C>AC>D on V bioleaching were obtained. The superiority of the developed hybrid ICA-MLP model over the developed RSM model in predicting Al and V dissolution was determined by NSE, RSME, MAE, and MEDAE. Parameters optimization and consequently evaluating optimum condition repeatability through three repeating experiments resulted in maximum dissolution recoveries of RSM: 96.5% for Al and 91.2% for V, and ICA-MLP: 97.1% for Al and 90.3% for V. Considering the lower relative errors of repeating validation tests, it can be concluded that, although both models provide reasonable results, but the ICA-MLP methodology is more reliable (relative error <1.8%).

LSTM-MLP BASED UNCERTAINTY MODELLING APPROACH FOR COMPLEX HUMAN INDOOR TRAJECTORY

Abstract. Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches.

Hybrid Multilayer Perceptron Network for Explosion Blast Prediction

For decades, scientists have studied the blast wave profile produced by an explosive detonation. Based on a significant amount of experimental data, the blast wave propagation profile has been predicted under given parameters. However, most studies have only looked at the central point of initiation for spherical form explosives. The purpose of this research is to compare the prediction performance of blast peak overpressure based on type of explosive, shape of explosive and point of detonation. The blast profiles of Emulex and PE-4, as well as to develop a prediction model using a Hybrid Multilayer Perceptron (HMLP) network. This experiment, which began at a distance of 1.2 m from the ground, employed a total of 500 grams of military explosive and Emulex. At distances of 0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, 3.0 m, 3.5 m and 4.0 m, the bomb was exploded. The Bayesian Regularization (BR) training algorithm is the best training algorithm for modelling Explosive Blast Prediction.

PSO-Based HMLP Network Controller for Speed Sensorless Control of PMSM

A sensorless control of permanent magnet synchronous motor (PMSM) by using model reference adaptive system (MRAS) and artificial neural network is presents in this paper. The objective for this study is to improve the speed and position estimation of the PMSM rotor which used the MRAS with the conventional PI controller. In this study, the multilayer perceptron (MLP) network and hybrid multilayer perceptron (HMLP) network from family of artificial neural network (ANN) were evaluated. Before the controller can be used, the value of weight between the network layers need to be optimised. To train the controller weight, particle swarm optimization (PSO) is used. Finally, the proposed method is evaluated by comparing with the proposed controller in controlling the speed and position of PMSM. Simulation results under various speed and load conditions indicated that the PSO-HMLP network controller achieved well results than the compared controller in terms transient response and overall system analysis.

Classification of Cervical Cancer Using Hybrid Multi-layered Perceptron Network Trained by Genetic Algorithm

Cervical cancer is well known as the third killer for women in Malaysia. The precancerous stage for detection can be determine by screening test, known Pap smear test for avoiding occurrence of cervical cancer. The problem face in the test is the human error in reading the data analysis and also lack number of pathologists to interpret the data analysis. Creating a computer-aided diagnosis system is one of the solutions that can interpret the data. Most of the research available used artificial neural network for diagnosis system to classify the cervical cancer cells data into normal and abnormal. This research creates a neural network (NN) using Hybrid Multi-layered Perceptron (HMLP) trained by Genetic Algorithm (GA) to diagnose the data. The data is extracted from cervical cells and divided into four features as the input, which are size of nucleus, size of cytoplasm, grey level of nucleus, and grey level of cytoplasm. The data is interpreted into three categories; are normal, Low-grade Squamous Intraepithelial Lesion (LSIL) and High-grade Squamous Intraepithelial Lesion (HSIL). These categories will be inserted in to the algorithm to calculate and determined the neural network performance. The data is randomly separated into dataset using 5-fold cross validation technique. The performance is compared with Hybrid Radial Basis Function (HRBF) trained with Adaptive Fuzzy K-means and Moving K-means Clustering Algorithm. This researched shows HMLP trained with GA create a better performance of the network in the accuracy, sensitivity, and specificity to be implemented in the cervical cancer for test the performance improvement.

Comparative study on activation function based heart abnormality activity

The detection of heart abnormality activity of a patient is important when an abnormal symptom occurred. This project is aimed to detect the disease by using hybrid multilayer perceptron (HMLP) network. Several data from the electrocardiogram (ECG) signal is extracted to be set as input parameters. In order to get the best result, several activation function are used such as logistic, hyperbolic, exponential, step and square root. The result obtained is then compared among the activation function and other detection techniques.

Heart abnormality detection by using artificial neural network

The detection of heart abnormality activity of a patient is important when an abnormal symptom occurred. This project is aimed to detect the disease by using Hybrid Multilayer Perceptron (HMLP) network. Several data from the ECG signal is extracted to be set as input parameters. In order to get the best result, the proposed training algorithm of HMLP network is by using Modified Recursive Prediction Error (MRPE). The result obtained is then compared with other training algorithm method to determine the best performance. The HMLP network is expected to give better performance compared with other network classifiers.

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.

ON-LINE MODELLING AND FORECASTING OF A MOVING CAR INFORMATION USING TD-HMLP NETWORK

The study proposed a model called trend data hybrid multilayered perceptron network (TD-HMLP) coupled with a modified recursive prediction error (MRPE) training algorithm as a nonlinear modeling. An on-line model was used to forecast speed, revolution and fuel balanced in a Proton Gen2 car tank. The car measured the injected fuel from fuel injection sensor and become an input for the TD-HMLP model to forecast the speed, revolution and fuel balanced in tank. These forecasted variables were also measured from the car sensors. The criterions for performances are based on the one step ahead forecasting (OSA), multi-step ahead forecasting (MSA) and adjusted R2. The forecasting result showed that TD-HMLP network is better than the conventional HMLP network to maintain higher value in adjusted R2 and produce better step in multi-step ahead forecasting. These preliminary results show that the proposed modeling approach is capable to be used as an on-line information forecaster of a moving car.

Modeling neural circuits in Parkinson's disease.

Parkinson's disease (PD) is caused by abnormal neural activity of the basal ganglia which are connected to the cerebral cortex in the brain surface through complex neural circuits. For a better understanding of the pathophysiological mechanisms of PD, it is important to identify the underlying PD neural circuits, and to pinpoint the precise nature of the crucial aberrations in these circuits. In this paper, the general architecture of a hybrid Multilayer Perceptron (MLP) network for modeling the neural circuits in PD is presented. The main idea of the proposed approach is to divide the parkinsonian neural circuitry system into three discrete subsystems: the external stimuli subsystem, the life-threatening events subsystem, and the basal ganglia subsystem. The proposed model, which includes the key roles of brain neural circuit in PD, is based on both feed-back and feed-forward neural networks. Specifically, a three-layer MLP neural network with feedback in the second layer was designed. The feedback in the second layer of this model simulates the dopamine modulatory effect of compacta on striatum.

Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design

In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

Adaptive Neuro-Controller Based on Hybrid Multi Layered Perceptron Network for Dynamic Systems

In this paper, an intelligent controller namely Adaptive Neuro-controller (ANC) based on Hybrid Multi Layered Perceptron (HMLP) network has been developed for dynamic systems. The performance of ANC has been compared with the ANC based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box (APBB) Controller. The comparison are based on the time response and the capability of the controlled output to track the model reference output. All controllers are based on a black box approach that offers simpler design approach. The Model Reference Adaptive System (MRAS) has been used to generate the desired output path and to ensure the output of the controlled system follows the output of the reference model. Weighted Recursive Least Square (WRLS) algorithm has been used to adjust the controller parameters in order to minimize the error between the plant output and the model reference output. The controllers have been tested using a linear plant and a nonlinear plant with several varying operating conditions such as varying gain, noise and disturbance. Based on the simulation results and performance analysis for all controllers, it is observed that ANC based on HMLP network is controllable and more stable than ANC based on MLP network and APBB controller. It is also can be signify that the ANC based on HMLP network is sufficient to control the plants with unpredictable conditions.

Detection of Tuberculosis Bacilli in Tissue Slide Images using HMLP Network Trained by Extreme Learning Machine

This paper proposes an automated detection of tuberculosis bacilli in Ziehl-Neelsen-stained tissue slides using image processing and neural network. Image segmentation using CY-based colour filter and k-mean clustering procedure is used to separate objects of interest from the background. A number of geometrical features are then extracted from the segmented images. A recent training algorithm called Extreme Learning Machine (ELM) is modified to train a hybrid multilayered perceptron network (HMLP) for the classification task. The results indicate that the performance of HMLP-ELM network is comparable to the previously proposed methods and offers a fast training time with no designing parameter required. Ill. 6, bibl. 15, tabl. 1 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.120.4.1456

Intelligent computer vision system for Sprague-Dawley rat sperm classification using gray-level microscopic images

This paper presents a development of intelligent computer vision system for Sprague-Dawley rat sperm classification. The system is developed to help pathologists and improve the current conventional detection methods and classification of Sprague-Dawley rat sperm. The conventional method tends to result in errors and is also time consuming. Thus, the new computer vision system is developed to overcome these problems and, consequently, produce more accurate results. The system is capable of classifying rat sperm into two groups, namely, normal and abnormal, based on the morphological characteristics of the sperm head. Furthermore, the system has the ability to classify the shapes of abnormal sperms which are banana and hookless shapes. The new system consists of four stages. In the first stage, sperms are segmented using the adaptive fuzzy moving K-means (AFMKM) clustering algorithm. In the second stage, a new automatic cropping algorithm was used to isolate the sperm head and to exclude other details. In the third stage, the feature extraction process was performed, wherein five features namely curvature angle, two flattened angles, curvature depth and number of curvatures are proposed to classify the sperm head of the rat. In the fourth (final) stage, the sperm head is classified using hybrid multilayered perceptron (HMLP) neural network trained with modified recursive prediction error (MRPE) algorithm. The developed system achieved an accuracy rate of 98.7%. Key words: Sprague-Dawley rat sperm, adaptive fuzzy moving K-means clustering algorithm, hybrid multilayered perceptron network, modified recursive prediction error algorithm.

Intelligent classification of cervical pre-cancerous cells based on the FTIR spectra

Inspired from the great potential of the Fourier-transform infrared (FTIR) spectroscopy as a screening tool for cervical cancer, this paper proposes an intelligent classification of cervical pre-cancerous cells based on the FTIR spectra. It consists of two parts: the extraction of FTIR characteristics and the intelligent classification of the pre-cancerous cells. Peak-corrected area-based features’ extraction (PCABFE) is introduced as a tool for the first part, while the Hybrid Multilayered Perceptron (HMLP) network is employed to classify the cervical pre-cancerous cells according to the Bethesda classification. Correlation test proves the capability of the proposed PCABFE to be as effective as the manual extraction by human experts, while the HMLP network produces a good classification performance with 97.4% of accuracy.

Sprague Dawley rat sperm classification using hybrid multilayered Perceptron network

As of now, the analysis of sperm such as counting and detection processes are still operated manually which tends to produce errors. False detection in sperm analysis must be minimized as possible. Therefore, the current study focuses on developing a Sprague Dawley rat sperm classification system to assist the detection process by pathologist. The system has the ability to classify the Sprague Dawley rat sperm into two classes namely normal and abnormal based on the morphological characteristic of sperm's head. The proposed system employs the Hybrid Multilayered Perceptron (HMLP) neural network trained with the Modified Recursive Prediction Error (MRPE) algorithm as the intelligent classifier tool. This study will also investigate three significant morphological characteristics of rat sperm's head namely opened degree, width of the curve and template matching percentage as the input data for the HMLP network. Furthermore, this study further classifies the abnormal sperm into hookless and banana shape. Promising result with 100% and 94.62% of accuracy has been achieved for two classes and three classes classification of rat sperm respectively.

INNOSAT ATTITUDE CONTROL SYSTEM BASED ON ADAPTIVE NEURO-CONTROLLER

The current research focuses on the designing of an intelligent controller for the Attitude Control System (ACS) of the Innovative Satellite (InnoSAT). The InnoSAT mission is to demonstrate local innovative space technology amongst the institutions of higher learning in the space sector. In this study, an Adaptive Neuro-controller (ANC) based on the Hybrid Multi Layered Perceptron (HMLP) network has been developed. The Model Reference Adaptive Control (MRAC) system is used as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model. The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output. The objective of this paper is to analyse the time response and the tracking performance of the ANC based on the HMLP network and the ANC based on the standard MLP network for controlling an InnoSAT attitude. These controllers have been tested using an InnoSAT model with some variations in operating conditions such as varying gain, measurement noise and disturbance torques. The simulation results indicated that the the ANC based on the HMLP network is adequate to control satellite attitude and give better results than the ANC based on the MLP network.

Clustered-Hybrid Multilayer Perceptron network for pattern recognition application

This paper introduces a modified version of the Hybrid Multilayer Perceptron (HMLP) network to improve the performance of the conventional HMLP network. We adopted the Clustering Algorithm from the Radial Basis Function (RBF) network architecture and incorporated it into the conventional HMLP network architecture. The modified model is called Clustered-Hybrid Multilayer Perceptron (Clustered-HMLP) network. The proposed Clustered-HMLP network architecture is trained using modified training algorithm called Clustered-Modified Recursive Prediction Error (Clustered-MRPE). The capability of the Clustered-HMLP network with Clustered-MRPE training algorithm is demonstrated using seven benchmark datasets from the University of California at Irvine (UCI) machine learning repository (i.e. Iris, Ionosphere, Pima Indian Diabetes, Wine, Lung Cancer, Hayes-Roth and Glass) and compared with the performance of other twelve classifiers reported in literature. Further, the new network is implemented to model a Transformer Fault Diagnosis System and Aggregate Shape Identification System. The results indicate that the proposed Clustered-HMLP network outperforms other eleven classifiers and provides a significant improvement to the conventional HMLP network for pattern recognition application.

Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

In this paper, a new learning algorithm, called the Modified Recursive Least Square (MRLS), is introduced for the Hybrid Multilayered Perceptron (HMLP) network. Adopting the Recursive Least Square (RLS) algorithm as its basis, the MRLS algorithm differs from RLS in the way that the weight of the linear connections for the HMLP network is estimated. The convergence rate of the MRLS algorithm is further improved by varying the forgetting factor, optimizing the way the momentum and learning rate are assigned. To investigate its applicability, the MRLS algorithm is demonstrated on the HMLP network using six benchmark data sets obtained from the UCI repository. The classification performance of the HMLP network trained with the MRLS algorithm is compared with those of the HMLP network trained with the Modified Recursive Prediction Error (MRPE) algorithm and the MLP trained with the standard RLS algorithm as well as with other commonly adopted machine learning classifiers. The comparison results indicated that the proposed MRLS trained HMLP network provides significant improvement over RLS trained MLP network, MRPE trained HMLP network, and other machine learning classifiers in terms of accuracy, convergence rate and mean square error (MSE).

Suitable features selection for the HMLP and MLP networks to identify the shape of aggregate

Aggregates are one of the main ingredients for producing concrete. In order to produce ‘high performance’ concrete, the properties of aggregates matter most. In particular, shape and surface texture of aggregates immensely influence the strength and structure of the resulting concrete. Often, low quality shape aggregates reduce the durability and strength of the concrete with much more requirement of sand, cement and water. Because the strength of concrete depends on the ratio of well-shaped aggregates to poor shape ones contained in the concrete mixture, the classification process for aggregates into well shaped and poor shaped is an essential step toward high quality concrete. Traditional technique requires aggregate to pass a stringent series of mechanical, chemical and physical test in order to demonstrate that they will perform satisfactorily, and meet or exceed specifications. Such stringent tests, often perform manually, tends to be slow, highly subjective and laborious. Therefore, an intelligent system that can separate the aggregates automatically could help to overcome these problems. This paper discusses an implementation of an intelligent classification system for the aggregate particles using neural network. The features obtained with image analysis, which are area, perimeter, Hu’s moments, Zernike’s moments of the aggregate’s area and perimeter were used as input data for the neural network. The hybrid multilayered perceptron network trained using modified recursive prediction error algorithm is employed to perform the classification. Performance analysis of the hybrid multilayered perceptron network is compared with the standard multilayered perceptron network trained using four different training algorithms, i.e. recursive prediction error, Bayesian Regulazation, Levenberg–Marquardt and scale conjugate gradient. The experimental results show that the performance for hybrid multilayered perceptron and multilayered perceptron networks are significantly better using the moments extracted based on object’s perimeter compared to object’s area. Model comparison confirms that the selected features combined with hybrid multilayered perceptron network attains the best performance in aggregates recognition.