32,768 research outputs found

    ANALISI NILAI PRODUKSI PADA PT INTAN PARIWARA MEDAN MENGGUNAKAN MULTILAYER PERCEPTRON NEURAL NETWORK

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    Multi layer perceptron banyak digunakan untuk menyelesaikan persoalan contohnya dalam menyelesaikan produksi suatu perusahaan disini penulis menggunakan perceptron untuk digunakan sebagai analisa suatu produksi dan memprediksi jumlah suatu barang yang bertujuan agar tidak terjadi kekurangan persediaan produksi dan dapat memenuhi permintaan Analisis dilakukan berdasarkan nilai bobot (w) dan threshold (θ) pada setiap input pada perhitungan manual dengan excel, dan hasil perhitungan tersebut sama dengan target produksi. Analisis juga dilakukan terhadap bobot yang diberikan pada setiap input dan dapat  dilihat dari nilai net (n). Tujuan dari analisa ini adalah mempunyai nilai net (n) berdasarkan hasil threshold (θ) menentukan bahwa nilai akan sama dengan target jika proses multiple – neuron perceptron pada hidden key mendapatkan nilai yang akurat

    Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

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    Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.Comment: 22 pages, 14 figure

    Structured Training for Neural Network Transition-Based Parsing

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    We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide in-depth ablative analysis to determine which aspects of our model provide the largest gains in accuracy

    A systematic comparison of supervised classifiers

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    Pattern recognition techniques have been employed in a myriad of industrial, medical, commercial and academic applications. To tackle such a diversity of data, many techniques have been devised. However, despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, the consideration of as many as possible techniques presents itself as an fundamental practice in applications aiming at high accuracy. Typical works comparing methods either emphasize the performance of a given algorithm in validation tests or systematically compare various algorithms, assuming that the practical use of these methods is done by experts. In many occasions, however, researchers have to deal with their practical classification tasks without an in-depth knowledge about the underlying mechanisms behind parameters. Actually, the adequate choice of classifiers and parameters alike in such practical circumstances constitutes a long-standing problem and is the subject of the current paper. We carried out a study on the performance of nine well-known classifiers implemented by the Weka framework and compared the dependence of the accuracy with their configuration parameter configurations. The analysis of performance with default parameters revealed that the k-nearest neighbors method exceeds by a large margin the other methods when high dimensional datasets are considered. When other configuration of parameters were allowed, we found that it is possible to improve the quality of SVM in more than 20% even if parameters are set randomly. Taken together, the investigation conducted in this paper suggests that, apart from the SVM implementation, Weka's default configuration of parameters provides an performance close the one achieved with the optimal configuration

    Spiking memristor logic gates are a type of time-variant perceptron

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    Memristors are low-power memory-holding resistors thought to be useful for neuromophic computing, which can compute via spike-interactions mediated through the device's short-term memory. Using interacting spikes, it is possible to build an AND gate that computes OR at the same time, similarly a full adder can be built that computes the arithmetical sum of its inputs. Here we show how these gates can be understood by modelling the memristors as a novel type of perceptron: one which is sensitive to input order. The memristor's memory can change the input weights for later inputs, and thus the memristor gates cannot be accurately described by a single perceptron, requiring either a network of time-invarient perceptrons or a complex time-varying self-reprogrammable perceptron. This work demonstrates the high functionality of memristor logic gates, and also that the addition of theasholding could enable the creation of a standard perceptron in hardware, which may have use in building neural net chips.Comment: 8 pages, 3 figures. Poster presentation at a conferenc
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