Speech Recognition Using HMM/ANN Hybrid Model

By the analysis on the principle of speech recognition system, a speech recognition system was designed by using LPC2148 as the hardware platform and MATLAB 2012 as the software platform. Speech recognition is an important component of biological identification which is an integrated technology of acoustics, signal processing and artificial intelligence. Recognition systems based on hidden Markov models are effective under particular circumstances, but do suffer from some major limitations that limit applicability of ASR technology in real-world environments. Attempts were made to overcome these limitations with the adoption of artificial neural networks as an alternative paradigm for ASR, but ANNs were unsuccessful in dealing with long time sequences of speech signals. So taking the limitations and advantages of both the systems it was proposed to combine HMM and ANN within a single, hybrid architecture. The goal in hybrid systems for ASR is to take advantage from the properties of both HMM and ANNs, improving flexibility and ASR performance For Speech recognition features from speech sample are extracted & mapping is done using Artificial Neural Networks. Multilayer pattern mapping neural network, which works on the principle of back propagation algorithm is proposed. Finally Speaker Recognition is done using Hidden Markov Model (HMM). The specialty of this model is the flexible and expandable hidden layer for recognition. DOI: 10.17762/ijritcc2321-8169.150613

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation using Bounded Probability Distributions and Hybrid Neural Networks

The penetration of renewable energy sources in modern power systems increases at an impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their generation including its uncertainty. In this article, an ensemble artificial neural network is applied for day ahead solar and wind power generation parametric probabilistic forecasting. The proposed architecture includes two components: a sub-models component and a Meta-Learner component. The first component includes an ensemble of artificial neural networks that have the ability to estimate the parameters of an underlying probability distribution. The Meta-Learner is responsible for grouping the training samples based on the estimated level of generation, through a classification-clustering process and use the output of the corresponding sub-models to calculate the final parametric probabilistic estimation. The proposed model is compared to both parametric and non-parametric state of the art probabilistic techniques for solar and wind power generation forecasting, exhibiting superior performance.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

A Minimal Architecture for General Cognition

A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to transfer architectural inspiration from biology into artificial intelligence, MANIC seeks to minimize novelty and follow the most well-established constructs that have evolved within various sub-fields of data science. From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence. This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International Joint Conference on Neural Network

Middleware platform for distributed applications incorporating robots, sensors and the cloud

Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented