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In the presentation major difficulties of designing neural networks are shown. It turn out that popular MLP (Multi Layer Perceptron) networks in most cases produces far from satisfactory results. Also, popular EBP (Error Back Propagation) algorithm is very slow and often is not capable to train best neural network architectures. Very powerful and fast LM (Levenberg- Marquardt) algorithm was unfortunately...
This paper presents the classification of ionic concentration using ion-sensitive field-effect transistor (ISFET) sensors with post-processing neural network ensemble. ISFETs are electrochemical potentiometric sensors that produce voltage response indicative of ionic concentration change. However, in the presence of ions of similar charge, the voltage levels tend to be influenced by the interfering...
The paper investigates the enhancement in various conjugate gradient training algorithms applied to a multilayer perceptron (MLP) neural network architecture. The paper investigates seven different conjugate gradient algorithms proposed by different researchers from 1952-2005, the classical batch back propagation, full-memory and memory-less BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithms...
This paper presents an optimizing methodology for implementing a multi-layer perceptron (MLP) neural network in a Field Programmable Gate Array (FPGA) device. In order to obtain an efficient implementation, a compromise of time and area is needed. Starting from simulation in the learning phase with fixed point operators, we have developed a methodology which allows the automatic generation of a VHDL...
The prediction of asthma that persists throughout childhood and into adulthood, in early life of a child has practical, clinical and prognostic implications and sets the basis for the future prevention. Artificial Neural Networks (ANNs) seems to be a superior tool for analyzing data sets where nonlinear relationships are existing between the input data and the predicted output. This study presents...
Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF)...
In this paper BLTA is extended to tackle the classification of Semi-Labeled data. BLTA works for Labeled data and perceptron based 4-layered neural network structure is formed. In our proposed extension, this 4-layered neural network structure works for classification of Semi-Labeled data, some samples are labeled and some are unlabeled. Learning algorithm is modified to tackle with such samples....
Automatic modulation recognition (AMR) of communication signals is a critical and challenging task in cognitive radio systems. In this work, classifications of four digital modulation types, including BPSK, QPSK, GMSK and 2FSK, are investigated. From the received radio signal, a set of cyclic spectrum features are first calculated, and a principal component analysis (PCA) is applied to extract the...
Multiple classifier systems or ensemble is an idea that is relevant both to neural computing and to machine learning community. Different MCSs can be designed for creating classifier ensembles with different combination functions. However, the best MCS can only be determined by performance evaluation. In this study, MCS is used to construct discriminant set that was used to discriminate the difficult...
In this work, we address the use of neural networks for nonlinear mixture modeling of hyperspectral data by focusing on different training strategies which can automatically generate mixed training samples without a priori information. The proposed approach is compared to the standard, fully constrained linear mixture model using a database of laboratory-simulated forest scenes acquired by the Compact...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
It is easy for a multi-layered perception (MLP) to form open plane classification borders, and for a radial basis function network (RBFN) to form closed circular or elliptic classification borders. In contrast, it is difficult for a MLP to form closed circular or elliptic classification borders, and for RBFN to form open plane classification borders. Hence, MLP and RBFN have their own advantages and...
This paper presents a Learning Classifier System (LCS) where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, providing the system with a flexible knowledge representation. It is shown how this approach allows for the evolution of networks...
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software...
In this paper, a novel neutral-network-based model is proposed to describe an X-Y macro-positioning stage. As the friction exists in the stage, the stage shows some complex behavior due to the non-smooth characteristic of the friction. In order to describe the non-smooth behavior of the stage, in this model, a non-smooth active function is proposed to construct the hidden neurons. Then, a training...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
In Malaysia, the screening coverage for cervical cancer is poor, which was at 2% in 1992, 3.5% in 1995, and at 6.2% in 1996, due to the shortage in pathologist workforce being one of the major cause. Study has been done before to overcome this by developing a diagnosis system based on neural networks, so that diagnosis can be done by an automated system with pathologist-like knowledge. Cell's features...
Motivated by the slow learning properties of multilayer perceptrons which utilize computationally intensive training algorithms and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN) and least-square support vector machines (LSSVM) technique. RPNN and LSSVM are combined with the finite element method (FEM), to evaluate the dielectric materials properties...
Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an Artificial Neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos...
The prediction of a microbend sensor response using Artificial Neural Networks (ANNs) has been investigated in this paper. Experiments were conducted with different microbend sensor configurations. By using the one experiment's input and output experimental data among the conducted experiments, the ability of the ANNs in the prediction of sensor response was analyzed. In the training process of the...
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