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951.
952.
A simplified neural network model is proposed to solve a class of linear matrix inequality problems. The stability and solvability
of the proposed neural network are analyzed and discussed theoretically. In comparison with the previous neural network models
(Lin and Huang, Neural Process Lett 11:153–169, 2000; Lin et al., IEEE Trans Neural Netw 11:1078–1092, 2000), the simplified
one is composed of two layers rather than three layers, and the neuron array in each layer is triangular rather than square.
The proposed approach can therefore reduce the complexity of the neural network architecture. In addition, the simplified
neural network can also be extended to solve multiple linear matrix inequalities with specific constraints, which enlarges
the application domain of the proposed approach. Finally, examples are given to illustrate the effectiveness and efficiency
of the simplified neural network. 相似文献
953.
This paper investigates an online gradient method with penalty for training feedforward neural networks with linear output.
A usual penalty is considered, which is a term proportional to the norm of the weights. The main contribution of this paper
is to theoretically prove the boundedness of the weights in the network training process. This boundedness is then used to
prove an almost sure convergence of the algorithm to the zero set of the gradient of the error function. 相似文献
954.
A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects
the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated
art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in
this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The
goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just
produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity,
change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual
network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
相似文献
Liane GaboraEmail: |
955.
Credit scoring using support vector machines with direct search for parameters selection 总被引:1,自引:1,他引:0
Ligang Zhou Kin Keung Lai Lean Yu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(2):149-155
Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the
model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring
model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment
(DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility
of the method. The results show that the direct search method can find the effective model with high classification accuracy
and good robustness and keep less dependency on the initial search space or point setting. 相似文献
956.
Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Muhammad Faiz Misman Michifumi Yoshioka 《Artificial Life and Robotics》2009,13(2):410-413
A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used
to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer
classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes
relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification.
It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this
paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried
on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach
that used a single-objective strategy in a genetic algorithm.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
957.
Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Muhammad Faiz Misman Michifumi Yoshioka 《Artificial Life and Robotics》2009,13(2):414-417
Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes
simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient
cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the
number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small
subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset
of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods
has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献
958.
Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by "authorized" users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system (IDS) is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference (KDD 2009) intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS (NGIDS) involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks. 相似文献
959.
In this paper, the pitch angle control of a lab model helicopter is discussed. This problem has some specific features. As a major unusual feature, it is observed that the steady state control command is completely dependent on the setpoint, and for different setpoints, different steady state control commands are needed to keep the error around zero. Moreover, the system is one with highly oscillating dynamics. In order to solve this control problem, two controllers are designed: an artificial neural network (ANN), whose input is the setpoint, is used to provide steady state control command, and a fuzzy inference system (FIS), whose input is error, is used to provide transient control command. The total control command is the sum of the two aforementioned control commands. It is proven that both ANN and FIS are boundary‐input boundary‐output (BIBO) systems. Using this fact and considering two experimental assumptions, the closed‐loop stability is also proven. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
960.
Efrat Blumenfeld-Lieberthal 《Networks and Spatial Economics》2009,9(3):427-458
This paper presents an analysis of the topology of transportation networks within different systems of cities. Urban entities
and their components are complex systems by their nature; there is no central force that affects their spatial structure.
Thus, we study transportation networks within different countries as complex networks. Based on the above, we consider cities
as nodes, while direct air and railways routes represent the links. We present characteristics of these networks including
their degree and clustering coefficient. Transportation networks can be used as an indicator of economic activity between
cities. Cities with strong economic relationship are characterized by high volume of connectivity. Our findings suggest that
the topology of the analyzed transportation networks can be used to classify the countries they belong to based on their economic
development.
相似文献
Efrat Blumenfeld-LieberthalEmail: |