3 edition of **Discrete sequence prediction and its applications** found in the catalog.

Discrete sequence prediction and its applications

- 244 Want to read
- 14 Currently reading

Published
**1992**
by NASA Ames Research Center, Artificial Intelligence Research Branch, National Technical Information Service, distributor in Moffett Field, CA, [Springfield, Va.?
.

Written in English

- Machine learning.

**Edition Notes**

Statement | Philip Laird. |

Series | Technical report -- FIA-92-01., NASA-TM -- 107865., Ho bun shiri zu -- FIA-92-01., NASA technical memorandum -- 107865. |

Contributions | Ames Research Center. Artificial Intelligence Research Branch. |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL14683603M |

The Kalman filter model assumes the true state at time k is evolved from the state at (k − 1) according to = − + + where F k is the state transition model which is applied to the previous state x k−1;; B k is the control-input model which is applied to the control vector u k;; w k is the process noise which is assumed to be drawn from a zero mean multivariate normal distribution,, with. Molecular Modeling of Proteins and Mathematical Prediction of Protein Structure. A heuristic method to bias protein's primary sequence in protein structure prediction. Signal Processing and Intelligent Parallelization of the Discrete Gradient Method of Non-smooth Optimization and Its Applications. Computational Science — ICCS Cited by:

Methods for Computational Gene Prediction was written with both molecular biologists and computer scientists in mind. Although those with training in math and statistics will find some of the material easier to grasp, the book starts out with both a math primer and background on molecular biology to bring both target audiences up to by: Mathematical induction, is a technique for proving results or establishing statements for natural part illustrates the method through a variety of examples. Definition. Mathematical Induction is a mathematical technique which is used to prove a statement, a formula or a theorem is true for every natural number.. The technique involves two steps to prove a statement, as stated.

review the recent applications of existing signal processing techniques, especially WT in DNA sequence analysis for sequence structure prediction, sequence comparison, and its classification. In Section III, we examine the existing signal processing techniques for DNA microarray analysis with the emphasis on the CNN for real-time DNA analysis. Then. needing new advances in discrete mathematics and its applications, for the following topics in computational molecular biology: DNA — fragment assembly, sequence alignment and comparison, motif discovery, optical mapping, word design, and chromosome rearrangements.

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Get this from a library. Discrete sequence prediction and its applications. [Philip D Laird; Ames Research Center. Artificial Intelligence Research Branch.].

Given a discrete alphabet Σ and a source producing an input sequence (s 1,s 2,s n) with s i ∈ Σ, the task of sequence prediction is to guess the successive sequence element s n+1.

Here each element s i may represent an object, a discrete event or any other discrete entity. Prediction with CMM is Author: Reinhard Blasig.

Sequence prediction is a popular task, which consists of predicting the next symbol of a sequence, given a set of training sequences.

Although numerous prediction models have been proposed, many have a low accuracy because they are lossy models (they discard information from training sequences to build the model), while lossless models are Author: Rafael Ktistakis, Philippe Fournier-Viger, Simon J.

Puglisi, Rajeev Raman. Based on Tables 2–4 and Figures 4–6, it can be found that the concave dynamic discrete GM (1,1) model obtains best results as compared to the GM (1,1) model, the discrete GM (1,1) model, and the optimized GM (1,1) model.

Comparison and Analysis. Section analyzes the fitting effect of conflict events in the process of urbanization in China based on several kinds of grey prediction Author: Ersi Liu, Qiangqiang Wang, Xinran Ge, Wei Zhou.

Discrete control is one of the major subsystems in the industrial automation and needs of the manufacturing sectors are the primary driver for the growth and maturity. A dedicated chapter is provided to detail the technology behind these products.

The discrete control and automation have started the journey with the sensors and relays wired to realize the logic.

Sequence Prediction A comparative study and implementation of sequence prediction techniques References: a. Laird, P.D., Saul, R. Discrete Sequence Prediction and Its Applications. Machine Learning, 15(1): () b.

Discrete mathematics is the study of mathematics confined to the set of integers. While the applications of fields of continuous mathematics such as calculus and algebra are obvious to many, the applications of discrete mathematics may at first be obscure.

Nevertheless, discrete. The term (W T – W to) will be normally distributed with mean zero and variance T – tby drawing replicas of this random variable, we can obtain exact replicas for S T at any T, t 0.

a discrete Hidden Markov Model (HMM) because the sequence of state that produces the observable data is not available (hidden). HMM can also be considered as a double stochastic process or a partially observed stochastic process.

Figure shows an example of a discrete HMM. Fi Now, let us consider the decoding the sequence of temples by: 1.

IPredict, a sequence prediction Framework. Sequence prediction consists in predicting the next item(s) of a sequence of items, given a set of training sequences and a finite alphabet of items (symbols).

This task has numerous applications such as web page prefetching, product recommendation, weather forecasting and stock market prediction. In order to deal with a mix of discrete and continuous action components, a first approach would be to use a fully continuous actor and somehow find a way to convert part of its continuous output.

PDF | In the empirical researches, the discrete GM (1,1) model is not always fitted well, and sometimes the forecasting error is large. In order to | Find, read. Discrete-Time Model Predictive Control. By Li Dai, Yuanqing Xia, Mengyin Fu and Magdi S. Mahmoud. problem, using the current state of the plant as the initial state.

Then the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant.

where p denotes the length of the prediction Cited by: 6. The Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications provides a comprehensive overview of theory and practice in simulation systems. A leading publication in this growing field, this Handbook of Research offers researchers, academicians, and practitioners progressive findings in simulation.

This is the first comprehensive treatment of the theoretical aspects of the discrete cosine transform (DCT), which is being recommended by various standards organizations, such as the CCITT, ISO etc., as the primary compression tool in digital image coding.

The main purpose of the book is to provide a complete source for the user of this signal processing tool, where both the basics and the.

famous text An Introduction to Probability Theory and Its Applications (New York: Wiley, ). In the preface, Feller wrote about his treatment of ﬂuctuation in coin tossing: “The results are so amazing and so at variance with common intuition that even sophisticated colleagues doubted that coins actually misbehave as theory by: To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures.

This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be.

Its essence is to keep using a discrete model to represent a protein yet without completely losing its sequence-order information. Therefore, in a broad sense, the PseAA composition of a protein is actually a set of discrete numbers that is derived from its amino acid sequence and that is different from the classical AA composition and able to.

A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and.

A discrete uncoupling process for finite spaces is introduced, called the Markov Cluster Process or the MCL process is the engine for the graph clustering algorithm called the MCL MCL process takes a stochastic matrix as input, and then alternates expansion and inflation, each step defining a stochastic matrix in terms of the previous by:.

Kalman filter was pioneered by Rudolf Emil Kalman inoriginally designed and developed to solve the navigation problem in Apollo Project. Since then, numerous applications were developed with the implementation of Kalman filter, such as applications in the fields of navigation and computer vision's object tracking.

Kalman filter consists of two separate processes, namely the prediction Cited by: 2.Information-Theoretic Methods for Prediction in the Wireless and Wired Web: /ch Discrete sequence modeling and prediction is an important goal and challenge for Web environments, both wired and wireless.

Web clients’ datarequestCited by: 2. These numbers have applications to lattice walks and RNA sequence prediction. The students required some basic background information from matrix algebra and combinatorial analysis. When constructing Riordan matrices, emphasis is placed on basic concepts related to generating functions (or formal power series).