An enhanced intelligent database engine by neural network and data mining

An Intelligent Database Engine (IDE) is developed to solve any classification problem by providing two integrated features: decision-making by a backpropagation (BP) neural network (NN) and decision support by Apriori, a data mining (DM) algorithm. Previous experimental results show the accuracy of NN (90%) and DM (60%) to be drastically distinct. Thus, efforts to improve DM accuracy is crucial to ensure a well-balanced hybrid architecture. The poor DM performance is caused by either too few rules or too many poor rules which are generated in the classifier. Thus, the first problem is curbed by generating multiple level rules, by incorporating multiple attribute support and level confidence to the initial Apriori. The second problem is tackled by implementing two strengthening procedures, confidence and Bayes verification to filter out the unpredictive rules. Experiments with more datasets are carried out to compare the performance of initial and improved Apriori. Great improvement is obtained for the latte

A New Method for Solving Supervised Data Classification Problems

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms

Adaptive GRNN for the modelling of dynamic plants

An integrated General Regression Neural Network (GRNN) adaptation scheme for dynamic plant modelling is proposed in this paper. It possesses several distinguished features compared to the original GRNN proposed by Specht [1], such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignment using non-statistical method, automatic target adjustment and sigma tuning. These adaptation strategies are formulated based on the inherent advantageous features found in GRNN, such as highly localised pattern nodes, good interpolation capability, instantaneous learning, etc.. Good modelling performance was obtained when the GRNN is tested on a linear plant in a noisy environment. It performs better than the well-known Extended Recursive Least Squares identification algorithm. In this paper, analysis on the effects of some of the adaptation parameters involving a nonlinear plant is also investigated. The results show that the proposed methodology is computationally efficient and exhibits several attractive features such as fast learning, flexible network sizing and good robustness, which are suitable for the construction of estimators or predictors for many model-based adaptive control strategies

Recursive least square and fuzzy modelling using genetic algorithm for process control application

A technique for the modelling of nonlinear process control using Recursive Least Square and Takagi-Sugeno Fuzzy System with Genetic Algorithm topology is described. This paper discusses the identification of parameters of the fuzzy sets at the antecedent part and linear model at the consequent part of fuzzy model within an application to process control. The key issues of finding the best model of the process are described. Results show that fuzzy model with genetic algorithm gives minimum mean squared error compare with recursive least square

Fault detection and diagnosis for process control rig using artificial intelligent

This paper focuses on the application of artificial intelligent techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a process control rig. Fuzzy logic with genetic algorithm method is used to develop fault model and to detect the fault where this task is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. Meanwhile, neural network is used for fault classification where this task is performed by identifying the fault in the system

Image analysis techniques for ripeness detection of palm oil fresh fruit bunches

Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably. Quality is an important factor that ensuring palm oil industries fulfill the demands of palm oil product. There has significant relationship between the quality of the palm oil fruits and the content of its oil. Ripe FFB gives more oil content, while unripe FFB give the least content. Overripe FFB shows that the content of oil is deteriorates. There have 4 classes of ripeness stages involves in this paper which are ripe, unripe, underipe and overripe. The proposed approach in this paper uses color features and bag of visual word for classifying oil palm fruit ripeness stages. Experiments conducted in this paper consisted of smartphone camera for image acquisition, python and matlab software for image pre processing and Support Vector Machine for classification. A total of 400 images is taken in a few plant in north Malaysia. Experiments involved on a dataset of 360 images for training for four classes and 40 images for testing. The average accuracy for the 4 classes of the FFB by color features is 57% while the accuracy for ripeness classification by using bag of visual word is 70%

Feature Level Fusion of Face and Signature Using a Modified Feature Selection Technique

The multimodal biometric which is a combination of two or more modalities of biometric is able to give more assurance for the securities of some systems. Feature level fusion has been shown to provide higher-performance accuracy and provide a more secure recognition system. In this paper, we propose a feature level fusion of face features which are the physical appearance of a person in image-based and the online handwritten signature features which are the behavioral characteristics of a person in dynamic-based. The problem of high dimensionality of the combined features is overcome by the used of Linear Discriminant Analysis (LDA) in the feature extraction phase. One challenge in multi modal feature level fusion is to maintain the balance of the features selected between the two modalities, otherwise one modality may outweigh another. In order to address this issue, we propose to perform feature fusion in the feature selection phase. Feature selection using GA with modified fitness function is applied to the concatenated features in order to ensure that only significant and most balanced features are used for classification. Comparison of the performance of the proposed method with other approaches indicates the highest in the recognition accuracy of 97.50%

Continuous stirred tank reactor fault detection using higher degree Cubature Kalman filter

Continuous Stirred Tank Reactor (CSTR) plays a major role in chemical industries, it ensures the process of mixing reactants according to the attended specification to produce a specific output. It is a complex process that usually represent with nonlinear model for benchmarking. Any abnormality, disturbance and unusual condition can easily interrupt the operations, especially fault. And this problem need to detect and rectify as soon as possible. A good knowledge based fault detection using available model require a good error residual between the measurement and the estimated state. Kalman filter is an example of a good estimator, and has been exploited in many researches to detect fault. In this paper, Higher degree Cubature Kalman Filter (HDCKF) is proposed as a method for fault detection by estimation the current state. Cubature Kalman filter (CKF) is an extension of the Kalman filter with the main purpose is to estimate process and measurement state with high nonlinearities. It is based on spherical radial integration to estimate current state by generating cubature points with specific value. Conventional CKF use 3rd degree spherical and 3rd degree radial, here we implement Higher Degree CKF (HDCKF) to have better accuracy as compared to conventional CKF. High accuracy is required to ensure no false alarm is detected and furthermore good computational cost will improve its detection. Finally, a numerical example of CSTR fault detection using HDCKF is presented. Implementation of HDCKF for fault detection is compared with other filter to show effective results

Enhancement of control’s parameter of decoupled HVAC system via adaptive controller through the system identification tool box

Heating, Ventilating and Air Conditioning (HVAC) systems have nonlinear character and nature. Current models for control components and the optimization of HVAC system parameters can be linear approximations based on an operating or activation point, or alternatively, highly complex nonlinear estimations. This duality creates problems when the systems are used with real time applications. The two parameters temperature and relative humidity (RH) have a more direct effect in most applications of HVAC systems than the execution. This study’s objective is to implement and simulate an adaptive controller for decoupled bi-linear HVAC systems for the purpose of controlling the temperature and RH in a thermal zone. The contribution of this study is to apply the adaptive controller for the decoupled bi linear HVAC system via relative gain array (RGA). To achieve this objective, we used a system identification toolbox to increase the speed and accuracy of the identification of system dynamics, as was required for simplification and decoupled HVAC systems. The method of decoupling is relative gain array. The results of the simulation show that when compared with a classical PID controller, the adaptive controller performance is superior, owing to the high efficiency with which the steady state set points for temperature and RH are reached