Mar 17, 2015. Backpropagation is a common method for training a neural network. You can play around with a Python script that I wrote that implements the backpropagation algorithm in this Github repo. For this tutorial, we’re going to use a neural network with two inputs, two hidden neurons. E-mail addresses: pi_enviro@comcast.net, dmillie@comcast.net (D.F. Contents lists. Selected to be a member of the National Marine Estuary Program for scientific research and education. (see Principe et al., 2000; Hu and Hwang, 2001) utilizing a back-propagation 'learning' algorithm were.

(d) means for performing a numerical optimization of the scalar multipliers c i which determine the weights identified with each hidden node ε 1, where i=1,...,t-(n+m), said optimization being performed in such a manner as to adjust the totality of all said multipliers c i so as to reduce deviation between the output values generated by propagating all inputs through the network to the final output nodes denoted ω j, j=1,... Blackberry 9320 Software Download 2. ,m and the desired output values Y k,j, k=1,...,p, j=1,...,m. ORIGIN OF THE INVENTION The invention described herein was made by an employee of the United States Government and may be manufactured and used by or for the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor. BACKGROUND OF THE INVENTION The back propagation neural network, or 'BPN', is an extremely useful neural network algorithm.

The BPN 'learns' a very general class of mappings which are usually represented as functions from R n to R m. Theoretically a 3-layer BPN can learn almost any map, but in practice the application of the BPN has been limited due to the enormous amount of computer time required for the training process. SUMMARY OF THE INVENTION The principal object of the present invention is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. Although the invention is, in principle, applicable to any neural network which implements supervised learning through error minimization or a so-called generalized delta rule, the neural network architecture for which the invention is best suited consists of a three-layer feed forward network having n1 inputs, n2 hidden units and n3 outputs. The invention contemplates that all learning will be supervised; i.e., correct outputs are known for all inputs in the training set. Brief Description of the Method: The training method according to the invention is applied to a feed-forward neural network having at least two layers of nodes, with a first layer having n1 nodes and a second layer having n2 nodes, each node of said second layer having a weight W2 i, where i=1,...,n2. Non-Patent Citations Reference 1 * Anton, Elementary Linear Algebra, Third Edition, 1981, pp.

4, 129, 186 187. 2 Anton, Elementary Linear Algebra, Third Edition, 1981, pp. 4, 129, 186-187. 3 Rummelhart et al, ' Parallel Distributed Processing, vol. I, Foundations, Rummelhart and McClelland, eds, pp.

318-362 (1986). 4 * Rummelhart et al, Learning Internal Representations by Error Propagation, Parallel Distributed Processing, vol. I, Foundations, Rummelhart and McClelland, eds, pp. 318 362 (1986). Referenced by Citing Patent Filing date Publication date Applicant Title * 23 Jun 1992 14 Feb 1995 Hitachi, Ltd. Learning method and apparatus for neural networks and simulator with neural network * 3 Nov 1992 5 Sep 1995 Bullock; Darcy M.

Neural network-based vehicle detection system and method * 5 Nov 1992 7 Nov 1995 Kabushiki Kaisha Toshiba Heuristic control system employing expert system, neural network and training pattern generating and controlling system * 9 Nov 1993 5 Dec 1995 At&T Ipm Corp. High efficiency learning network * 18 Oct 1993 28 Jan 1997 Loma Linda University Medical Center Self organizing adaptive replicate (SOAR) * 7 Jul 1995 3 Jun 1997 Ricoh Corporation Method for operating an optimal weight pruning apparatus for designing artificial neural networks * 5 Jun 1995 15 Jul 1997 U.S. Philips Corporation Neural device and method of constructing the device * 31 Aug 1993 12 May 1998 Werbos; Paul J. Elastic fuzzy logic system * 6 Feb 1995 18 Aug 1998 U.S. Philips Corporation Neural digital processor utilizing an approximation of a non-linear activation function * 7 Jul 1997 1 Sep 1998 U.S. Philips Corporation Method for constructing a neural device for classification of objects * 25 Oct 2004 1 Apr 2008 Ford Global Technologies, Llc System and method for detecting presence of a human in a vehicle * 24 May 2002 7 Dec 2010 Oracle International Corporation Intelligent sampling for neural network data mining models 2 Mar 2013 7 Apr 2015 Kontek Industries, Inc.

PURPOSE: Fatty liver disease (FLD) is one of the most common diseases in liver. Early detection can improve the prognosis considerably. Using ultrasound for FLD detection is highly desirable due to its non-radiation nature, low cost and easy use. However, the results can be slow and ambiguous due to manual detection. The lack of computer trained systems leads to low image quality and inefficient disease classification. Thus, the current study proposes novel, accurate and reliable detection system for the FLD using computer-based training system. MATERIALS AND METHODS: One hundred twenty-four ultrasound sample images were selected retrospectively from a database of 62 patients consisting of normal and cancerous.

The proposed training system was generated offline parameters using training liver image database. The classifier applied transformation parameters to an online system in order to facilitate real-time detection during the ultrasound scan.

The system utilized six sets of features (a total of 128 features), namely Haralick, basic geometric, Fourier transform, discrete cosine transform, Gupta transform and Gabor transform. These features were extracted for both offline training and online testing. Levenberg-Marquardt back propagation network (BPN) classifier was used to classify the liver disease into normal and abnormal categories. RESULTS: Random partitioning approach was adapted to evaluate the classifier performance and compute its accuracy. Utilizing all the six sets of 128 features, the computer aided diagnosis (CAD) system achieved classification accuracy of 97.58%.

Furthermore, the four performance metrics consisting of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) realized 98.08%, 97.22%, 96.23%, and 98.59%, respectively. Boost 2 Apk Free Download. CONCLUSION: The proposed system was successfully able to detect and classify the FLD. Furthermore, the proposed system was benchmarked against previous methods. The comparison established an advanced set of features in the Levenberg-Marquardt back propagation network reports a significant improvement compared to the existing techniques.

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