The study of system dynamics starts from model construction and simulation to understand and solve dynamical complicated problems. Traditionally approaches of modeling process depend on an expert¡¦s experiences and the trial & error procedure. Chen¡¦s research proposes a transformation method that could map a System Dynamics Model (SDM) to a specially designed Partial Recurrent Network (PRN). Thus he could use the neural network training algorithm to assist model construction and policy design. In this paper, we will introduce a Genetic Algorithm (GA) in the model building process, which encodes a PRN into a string and uses an evolution process to select a best solution. The algorithm not only improves the PRN training, but also generates more candidate models for consideration. Thus, it enhances the SDM-PRN transformation¡¦s usability.

Traffic congestion has become a menace to civilized society. It degrades air quality, jeopardizes safety and causes delay. Traffic congestion can be alleviated by providing an effective traffic control signal system. Closed-loop traffic control systems are an example of such a system. Closed-loop traffic control systems can be operated primarily in either of two modes: Time of Day Mode (TOD) or Traffic Responsive Plan Selection Mode (TRPS). TRPS mode, if properly configured, can easily handle time independent variation in traffic volumes. It can also reduce the effect of timing plan aging. Despite these advantages, TRPS mode is not used as frequently as TOD mode. The reason being a lack of methodologies and formal guidelines for predicting the factors and thresholds associated with TRPS mode. In this research, a new methodology is developed for determining the thresholds and factors associated with the TRPS mode. This methodology, when tested on a closed-loop system in Odem, Texas, produced a classification accuracy of 94%. The classification accuracy can be increased to 98% with a proposed TRPS architecture.

<p>The aim of inverting artificial neural networks (ANNs) is to find input patterns that are strongly classified as a predefined class. In this project an ANN is inverted by an evolutionary algorithm. The network is retrained by using the patterns extracted by the inversion as counter-examples, i.e. to classify the patterns as belonging to no class, which is the opposite of what the network previously did. The hypothesis is that the counter-examples extracted by the inversion will cause larger updates of the weights of the ANN and create a better mapping than what is caused by retraining using randomly generated counter-examples. This hypothesis is tested on recognition of pictures of handwritten digits. The tests indicate that this hypothesis is correct. However, the test- and training errors are higher when retraining using counter-examples, than for training only on examples of clean digits. It can be concluded that the counter-examples generated by the inversion have a great impact on the network. It is still unclear whether the quality of the network can be improved using this method.</p>

<p>This dissertation evaluates the artificial neural technique for evolving a smart antenna system. The AI techniques pose a challenging research in the field of communication. As such the antennas help to communicate with the digital processor to choose the desired signals and reject the others. It makes its own decision even to find the level of interferences and noises to be discarded by amplitude elimination process through the use of perceptron optimization algorithms like LMS (Least Mean Squares). This method helps to enhance the performance of signal processing efficiently. The design of hardware and software are quite complex. This is due to the fact, that the behaviour of the system is not fully understood being a real-time dependent system. This research work is carried only on software with certain simulated activity on beam-formation algorithm and as well, the system responses before and after using the adaptive algorithm. In this report, we try to concentrate to work on the method of adaptivity to make antenna adaptable to a virtual form of real-time environment. For, this reason a two-element antenna is used for simulation testing, as it is the most commonly used antenna for all purposes in communication. It is also tested on various scanning levels of rotation to determine the learning rate (a parameter that has no effect on the radiation output after using LMS) mean-square error rates and convergence analysis. For the purpose of above mentioned tests, three hypotheses are framed in relation to side-lobe reduction level above 5 decibels, the narrowing of the beam after adaptivity and finally the response of the main beam output for varying values of learning rate, respectivelty. The given research work, may comprehend good practical use of this LMS algorithm and also to indicate antenna patterns and the responses to adaptivity conditions through clarity in graphical format.</p><p>The method is influenced to reduce computational complexity and bring simplicity to the functionality of the antenna with more efficient and effective adaptivness. An effort to test theoretical concepts in practice is also been made in this thesis work. The results show that the antenna system can be made to evolve itself through the process of adaptation with simple behaviour by relying on artificial intelligence technique which ensures little supervision and human intereference. Eventually, it is understood that the reader should have possessed prior concepts, related to antennas, digital signal processing and its practical usage in artificially intelligent systems and as well the exceptions in it, since the work is explained in the direct level assuming so.</p>

Non-recurring congestion caused by incidents is a major source of traffic delay in freeway systems. With the objective of reducing these traffic delays, traffic operation managers are focusing on detecting incident conditions and dispatching emergency management teams to the scene quickly. During the past few decades, a few number of conventional algorithms and artificial neural network models were proposed to automate the process of detecting incident conditions on freeways. These algorithms and models, known as automatic incident detection methods (AIDM), have experienced a varying degree of detection capability. Of these AIDMs, artificial neural network-based approaches have illustrated better detection performance than the conventional approaches such as filtering techniques, decision tree method, and catastrophe theory. So far, a few neural network model structures have been tested to detect freeway incidents. Since the freeway incidents directly affect the freeway traffic flow, majority of these models have used only traffic flow variables as model inputs. However, changes in traffic flow may also be stimulated by the other features (e.g., freeway geometry) to a greater extent. Many AIDMs have also used a conventional detection rate as a performance measure to assess the detection capability. Yet the principle function of incident detection model, which is to identify whether an incident condition exists for a given traffic pattern, is not measured in its entirety by this conventional measure. In this study, new input feature sets, including freeway geometry information, were proposed for freeway incident detection. Sixteen different artificial neural network (ANN) models based on feed forward and recurrent architectures with a variety of input feature sets were developed. ANN models with single and double hidden layers were investigated for incident detection performance. A modified form of a conventional detection rate was introduced to capture full capability of AIDMs in detecting incident patterns in the freeway traffic flow. Results of this study suggest that double hidden layer networks are better than single hidden layer networks. The study has demonstrated the potential of ANNs to improve the reliability using double layer networks when freeway geometric information is included in the model.

Transposons are DNA segments that can move or transpose themselves to new positions within the genome of an organism. Biologists need to predict preferred insertion sites of transposons to devise strategies in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called Vstep, is of significant importance in the target-site selection process. We considered the Vstep profiles of insertion sites and developed predictors based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM). We trained our ANN and SVM predictors with the Sleeping Beauty transposonal data, and used them for identifying preferred individual insertion sites (each 12bp in length) and regions (each 100bp in length). Running a five-fold cross-validation showed that (1) Both ANN and SVM predictors are more successful in recognizing preferred regions than preferred individual sites; (2) Both ANN and SVM predictors have excellent performance in finding the most preferred regions (more than 90% sensitivity and specificity); and (3) The SVM predictor outperforms the ANN predictor in recognizing preferred individual sites and regions. The SVM has 83% sensitivity and 72% specificity in identifying preferred individual insertion sites, and 85% sensitivity and 90% specificity in recognizing preferred insertion regions.

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No description available.

Transposons are DNA segments that can move or transpose themselves to new positions within the genome of an organism. Biologists need to predict preferred insertion sites of transposons to devise strategies in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called Vstep, is of significant importance in the target-site selection process. We considered the Vstep profiles of insertion sites and developed predictors based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM). We trained our ANN and SVM predictors with the Sleeping Beauty transposonal data, and used them for identifying preferred individual insertion sites (each 12bp in length) and regions (each 100bp in length). Running a five-fold cross-validation showed that (1) Both ANN and SVM predictors are more successful in recognizing preferred regions than preferred individual sites; (2) Both ANN and SVM predictors have excellent performance in finding the most preferred regions (more than 90% sensitivity and specificity); and (3) The SVM predictor outperforms the ANN predictor in recognizing preferred individual sites and regions. The SVM has 83% sensitivity and 72% specificity in identifying preferred individual insertion sites, and 85% sensitivity and 90% specificity in recognizing preferred insertion regions.