STRATEGY MANAGEMENT IN A MULTI-AGENT SYSTEM USING NEURAL NETWORKS FOR INDUCTIVE AND EXPERIENCE-BASED LEARNING

Intelligent agents and multi-agent systems prove to be a promising paradigm for solving problems in a distributed, cooperative way. Neural networks are a classical solution for ensuring the learning ability of agents. In this paper, we analyse a multi-agent system where agents use different training algorithms and different topologies for their neural networks, which they use to solve classification and regression problems provided by a user. Out of the three training algorithms under investigation, Backpropagation, Quickprop and Rprop, the first demonstrates inferior performance to the other two when considered in isolation. However, by optimizing the strategy of accepting or rejecting tasks, Backpropagation agents succeed in outperforming the other types of agents in terms of the total utility gained. This strategy is learned also with a neural network, by processing the results of past experiences. Therefore, we show a way in which agents can use neural network models for both external purposes and internal ones.agents, learning, neural networks, strategy management multi-agent system.

The Improvement of Neural Network Cascade-Correlation Algorithm and Its Application in Picking Seismic First Break

Neural Network is a kind of widely used seismic wave travel time auto-picking method. Most commercial software such as Promax often uses Back Propagation (BP) neural network. Here we introduce a cascade-correlation algorithm for constructing neural network. The algorithm’s convergence is faster than BP algorithm and can determine its own network architecture according to training samples, in addition, it can be able to expand network topology to learn new samples. The cascaded-correlation algorithm is improved. Different from the standard cascade-correlation algorithm, improved algorithm starts at an appropriate BP network architecture (exits hidden units), but the standard one’s initial network only includes input layer and output layer. In addition, in order to prevent weight-illgrowth, adding regularization term to the objective function when training candidate hidden units can decay weights. The simulation experiment demonstrates that improved cascade-correlation algorithm is faster convergence speed and stronger generalization ability. Analytically study five attributes, including instantaneous intensity ratio, amplitude, frequency, curve length ratio, adjacent seismic channel correlation. Intersection figure shows that these five attributes have distinctiveness of first break and stability. The neural network first break picking method of this paper has achieved good effect in testing actual seismic data.Key words: Neural network; Cascade-correlation algorithm; Picking seismic first brea

Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks

In this study, particulate matter (PM), total suspended particulate (TSP), PM10, and PM2.5 fractions) concentrations were recorded in various cities from south of Romania to build the corresponding time series for various intervals. First, the time series of each pollutant were used as inputs in various configurations of feed-forward neural networks (FANN) to find the most suitable network architecture to the PM specificity. The outputs were evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Pearson correlation coefficient (r) between observed series and output series. Second, each time series was decomposed using Daubechies wavelets of third order into its corresponding components. Each decomposed component of a PM time series was used as input in the optimal feed-forward neural networks (FANN) architecture established in the first step. The output of each component was re-included to form the modeled series of the original pollutant time series

Supervised learning in Spiking Neural Networks with Limited Precision: SNN/LP

A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large scale hardware neural networks. One of the trained networks is implemented in programmable hardware.Comment: 7 pages, originally submitted to IJCNN 201

Optical imaging of cloud-to-stratosphere/mesosphere lightning over the Amazon Basin (CS/LAB)

The purpose of the CS/LAB project was to obtain images of cloud to stratosphere lightning discharges from aboard NASA's DC-8 Airborne Laboratory while flying in the vicinity of thunderstorms over the Amazon Basin. We devised a low light level imaging package as an add-on experiment to an airborne Laboratory deployment to South America during May-June, 1993. We were not successful in obtaining the desired images during the South American deployment. However, in a follow up flight over the American Midwest during the night of July 8-9, 1993 we recorded nineteen examples of the events over intense thunderstorms. From the observations were estimated absolute brightness, terminal altitudes, flash duration, horizontal extents, emission volumes, and frequencies relative to negative and positive ground strokes

XFSL: A tool for supervised learning of fuzzy systems

This paper presents Xfsl, a tool for the automatic tuning of fuzzy systems using supervised learning algorithms. The tool provides a wide set of learning algorithms, which can be used to tune complex systems. An important issue is that Xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0, and hence, it can be easily employed within the design flow of a fuzzy system.Comisión Interministerial de Ciencia y Tecnología TIC98-0869Fondo Europeo de Desarrollo Regional 1FD97-0956-C3-0

Automated Propulsion Data Screening demonstration system

A fully-instrumented firing of a propulsion system typically generates a very large quantity of data. In the case of the Space Shuttle Main Engine (SSME), data analysis from ground tests and flights is currently a labor-intensive process. Human experts spend a great deal of time examining the large volume of sensor data generated by each engine firing. These experts look for any anomalies in the data which might indicate engine conditions warranting further investigation. The contract effort was to develop a 'first-cut' screening system for application to SSME engine firings that would identify the relatively small volume of data which is unusual or anomalous in some way. With such a system, limited and expensive human resources could focus on this small volume of unusual data for thorough analysis. The overall project objective was to develop a fully operational Automated Propulsion Data Screening (APDS) system with the capability of detecting significant trends and anomalies in transient and steady-state data. However, the effort limited screening of transient data to ground test data for throttle-down cases typical of the 3-g acceleration, and for engine throttling required to reach the maximum dynamic pressure limits imposed on the Space Shuttle. This APDS is based on neural networks designed to detect anomalies in propulsion system data that are not part of the data used for neural network training. The delivered system allows engineers to build their own screening sets for application to completed or planned firings of the SSME. ERC developers also built some generic screening sets that NASA engineers could apply immediately to their data analysis efforts