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In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the convergence of a new back-propagation algorithm with adaptive momentum is analyzed when it is used for training feedforward neural networks with a hidden layer. A convergence theorem is presented and sufficient conditions are offered to guarantee both weak and strong convergence result. Compared with existing results, our convergence result is of deterministic nature and we do not require the error function to be quadratic or uniformly convex. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks have some applications in computerized tomography, in particular to reconstruct an image from projections. The presented paper describes a new practical approach to the reconstruction problem using a Hopfield-type neural network. The methodology of this reconstruction algorithm resembles a transformation formula—the so-called ρ-filtered layergram method. The method proposed in this work is adapted for discrete fan beam projections, already used in practice. Performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in obtained image quality, and in perspective of hardware implementation in the speed of the reconstruction process. [Copyright &y& Elsevier]

Abstract: Unlike to traditional hierarchical and partitional clustering algorithms which always fail to deal with very large databases, a neural network architecture, projective adaptive resonance theory (PART), is developed for the high dimensional space clustering. However, the success of the PART algorithm depends on both accurate parameters and satisfied orders of input data sets. These disadvantages prevent PART from being applied to realtime databases. In this paper, we propose an improved method, PART with buffer management, to overcome these disadvantages. The major contributions of our method are introducing a buffer management and a new similar degree function and buffer checkout process. The buffer management mechanism allows data sets not to be immediately clustered to one cluster. The purpose of the average similar degree is to successfully work with high similar noise data sets and partly achieve an order-independent objective without correct parameters. And the average similar degree has a good attribute, the parameter-tolerance. Namely, the clustering result does not depend on the precise choice of input parameters, and different parameter values have close clustering results including dimensions associated with clusters. The buffer checkout process can handle a huge amount of input data sets by a small buffer space. Also, simulations and comparisons in high dimensional spaces are reported, and an application by using our algorithm to find stock concurrence association rules is given finally. [Copyright &y& Elsevier]

Abstract: The term neural network evolution usually refers to network topology evolution leaving the network''s parameters to be trained using conventional algorithms. In this paper we present a new method for neural network evolution that evolves the network topology along with the network parameters. The proposed method uses grammatical evolution to encode both the network and the parameters space. This allows for a better description of the network using a formal grammar allowing the network architect to shape the resulting search space in order to meet each problem requirement. The proposed method is compared with other three methods for neural network training and is evaluated using 9 known classification problems and 9 known regression problems. In all 18 datasets, the proposed method outperforms its competitors. [Copyright &y& Elsevier]

Abstract: This paper presents the design of a hybrid learning model, termed as neural network tree (NNTree). It incorporates the advantages of both decision tree and neural network. An NNTree is a decision tree, where each non-terminal node contains a neural network. The idea of the proposed method is to use the framework of multilayer perceptron to design tree-structured pattern classifier. At each non-terminal node, the multilayer perceptron partitions the dataset into subsets, being the number of classes in the dataset present at that node. The NNTree is designed by splitting the non-terminal nodes of the tree by maximizing classification accuracy of the multilayer perceptron. In effect, it produces a reduced height -ary tree. The versatility of the proposed scheme is illustrated through its application in diverse fields. The effectiveness of the hybrid algorithm, along with a comparison with other related algorithms, has been demonstrated on a set of benchmark datasets. Simulation results show that the NNTree achieves excellent performance in terms of classification accuracy, size of the tree, and classification time. [Copyright &y& Elsevier]

Abstract: This paper presents a novel neural network learning algorithm, the tabu-based neural network learning algorithm (TBBP). In our work, the TBBP mainly use the tabu search (TS) to improve the nonlinear function approximating ability of the neural network. By using the TS in the global search, the algorithm can escape from the local minima and obtain some superior global solutions, the weights of the neural network, to approximate the nonlinear function. Results confirm that the TBBP can greatly improve the approximating ability of the neural network for several typical nonlinear functions. [Copyright &y& Elsevier]

Abstract: In this paper we present two novel image restoration algorithms based on a modified Hopfield neural network and variational partial differential equations (PDE). The first algorithm is based on a harmonic model and the other is based on a total variation model. Both algorithms can restore the degraded images and preserve the edges. The experimental results show that our algorithms are better than other known neural network-based restoration algorithms, especially when images mainly consist of edges, such as letters. [Copyright &y& Elsevier]

Abstract: In this paper, we present an improved version of the online phase-space learning algorithm of Tsung and Cottrell (1995), called ARTISTE (Autonomous Real-TIme Selection of Training Examples). The new version''s advantages derive from an online adaptive learning rate that depends on the error. We demonstrate the algorithm''s efficacy on two problems: learning a pair of sine waves offset by 90° and the van der Pol oscillator. The online version of the algorithm allows the system to learn as it behaves. We show that the adaptive learning rate technique gives us excellent results in the learning of the above two tasks. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a neural network-based decision table algorithm. We focus on the implementation details of the decision table algorithm when it is constructed using the neural network. Decision tables are simple supervised classifiers which, Kohavi demonstrated, can outperform state-of-the-art classifiers such as C4.5. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. Initially, we demonstrate how the binary associative-memory neural network can form the decision table index to map between attribute values and data records and subsequently we show how two attribute selection algorithms can be used to pre-select attributes for this decision table. The attribute selection algorithms are easily implemented within the same binary associative-memory framework producing a tightly coupled, two-tier system allowing attribute selection and decision table indexing. The first attribute selector uses mutual information between attributes and classes to select the attributes that classify best. The second attribute selector uses a probabilistic approach to evaluate randomly selected attribute subsets. [Copyright &y& Elsevier]

Abstract: This paper presents a blind watermarking algorithm based on the Hopfield neural network. The watermarking capacity of this algorithm is discussed. According to the research, the attraction basin of neural network determines the watermarking capacity. [Copyright &y& Elsevier]