Data prediction using ANNs in MATLAB Simulink with sampled data.
Journal name: World Journal of Pharmaceutical Research
Original article title: Data prediction from a set of sampled data using artificial neural network in matlab simulink
The WJPR includes peer-reviewed publications such as scientific research papers, reports, review articles, company news, thesis reports and case studies in areas of Biology, Pharmaceutical industries and Chemical technology while incorporating ancient fields of knowledge such combining Ayurveda with scientific data.
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Md. Shohel Rana and Shakila Zahan
World Journal of Pharmaceutical Research:
(An ISO 9001:2015 Certified International Journal)
Full text available for: Data prediction from a set of sampled data using artificial neural network in matlab simulink
Source type: An International Peer Reviewed Journal for Pharmaceutical and Medical and Scientific Research
Doi: 10.20959/wjpr201818-13607
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Summary of article contents:
1. Introduction
The paper discusses the application of Artificial Neural Networks (ANNs) in data prediction, emphasizing their advantages over classical statistical methods like ARIMA, which assume linear relationships between inputs and outputs. ANNs are beneficial for modeling complex, nonlinear interactions inherent in real-world data influenced by various economic and environmental factors. This research compares different neural network architectures (particularly feed-forward and recurrent networks) and their performance in predicting foreign exchange rates, aiming to identify the most effective method for such predictive tasks.
2. The Power of Neural Networks
One of the core concepts explored in this paper is the classification and predictive capabilities of Neural Networks. Research has demonstrated that ANNs can approximate any continuous function, making them powerful tools for forecasting tasks. Unlike traditional statistical models, neural networks do not require prior knowledge of the data properties and can adaptively learn relationships from the data itself. This self-learning ability allows ANNs to capture complex patterns and trends within the dataset, providing a significant advantage in various predictive tasks, including time series forecasting.
3. Structure and Functionality of Neural Networks
The structure of ANNs consists of multiple interconnected layers: the input layer, hidden layers, and output layer. Each layer plays a critical role in transforming the input data into a meaningful output. The hidden layers, which contain numerous neurons, implement complex mathematical functions that modify the input data before it reaches the output layer. This multi-layered approach enables the network to efficiently learn nonlinear relationships, thus enhancing its predictive capacity across a multitude of applications, including sequence prediction and time series data.
4. Methodological Approach to Data Prediction
The methodology behind using ANNs for data prediction involves several iterative steps that include data collection, network creation and configuration, initial weight setting, training, validation, and application of the trained model. A significant advantage of ANNs is their ability to learn solely from examples, requiring no additional information. Furthermore, they can discern hidden dependencies within the data, outperforming traditional models that may struggle with more complex relationships. However, challenges remain in determining the extent of learned dependencies and predicting potential errors.
5. Conclusion
In conclusion, the study illustrates that neural networks are effective tools for prediction, particularly due to their ability to learn directly from historical data without the necessity of explicit models. They can generalize well and are robust against noise, making them suitable for various applications, especially where traditional methods may fall short. Despite challenges in understanding their internal learning mechanisms and estimating predictive errors, neural networks have proven their utility in forecasting and remain an area of significant potential in data prediction disciplines.
FAQ section (important questions/answers):
What are the advantages of using Neural Networks for prediction?
Neural Networks can automatically learn complex, non-linear dependencies from data without needing explicit models or additional information, making them particularly effective in various predictive tasks.
How does MATLAB support Neural Network training and implementation?
MATLAB offers a user-friendly environment with built-in functions to create, configure, and train Neural Networks, enabling efficient data analysis, algorithm development, and modeling.
What are the main challenges in data prediction using Neural Networks?
Challenges include handling noise in data, understanding learned relationships, and accurately estimating prediction errors, as well as the need for sufficient training data to ensure generalization.
What is the structure of a typical Neural Network?
A typical Neural Network consists of an input layer, one or more hidden layers with nonlinear activation functions, and an output layer that produces final predictions.
What types of data can be predicted using Neural Networks?
Neural Networks are particularly effective for time series data but can also be used for predicting trends and other types of continuous values influenced by various factors.
Why is preprocessing data important for Neural Network training?
Preprocessing helps normalize inputs to avoid saturation in activation functions, improves training efficiency, and ensures that the network learns effectively from the data provided.
Glossary definitions and references:
Scientific and Ayurvedic Glossary list for “Data prediction using ANNs in MATLAB Simulink with sampled data.”. This list explains important keywords that occur in this article and links it to the glossary for a better understanding of that concept in the context of Ayurveda and other topics.
1) Training:
Training in the context of neural networks refers to the process of adjusting the network's parameters using data. It involves inputting examples so the model can learn patterns, minimizing prediction errors. Effective training is crucial for creating a reliable model that accurately predicts outcomes based on new data inputs.
2) Rana:
Rana is the name of the corresponding author of the study presented in the paper. Identifying the author is important as it links the research to a specific individual responsible for the work, providing a point of contact for inquiries and a measure of credibility to the research outcomes.
3) Language:
Language is a system of communication using symbols, sounds, or gestures that allows individuals to convey information and express emotions. In programming, languages like MATLAB are used to implement algorithms and processes, including neural networks. The choice of language affects the efficiency and clarity of code in computational tasks.
4) Learning:
Learning refers to the process by which artificial neural networks improve their performance through exposure to data. It involves adjusting weights and biases within the network based on the error of predictions compared to actual outcomes. Effective learning enables models to generalize from training data to unseen data effectively.
5) Science (Scientific):
Science is the systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Neural networks play a role in scientific research by facilitating the analysis of complex data sets and enabling scientific predictions, thereby contributing to advancements in various fields.
6) Performance:
Performance describes how effectively a neural network fulfills its intended purpose, specifically accurate predictions. It can be measured by evaluating various metrics such as accuracy, precision, and recall. Analyzing performance helps identify the strengths and weaknesses of the model and informs necessary adjustments for improved results.
7) Inference:
Inference involves drawing conclusions based on evidence and reasoning within the context of statistical modeling and neural networks. It can relate to predicting future outcomes or understanding relationships within data. In predictive modeling, inference often indicates the model's ability to generalize findings from the training data to new examples.
8) Field:
Field refers to a specific domain of study or professional practice. In this context, the field pertains to statistics, machine learning, neural networks, and their applications. The evolution of this field has significant implications for technology, commerce, and other areas where data analysis and predictive modeling are essential.
9) Line:
Line, in the context of this paper, can refer to the running text as well as representing data points in a graphical output analysis. In data visualization, a line graph can succinctly illustrate trends and patterns over time, making complex data more comprehensible at first glance.
10) Noise:
Noise refers to random variations and disturbances in the data that can lead to inaccurate predictions in neural networks. Understanding how to minimize and manage noise is essential for improving model accuracy. High levels of noise can obscure the true patterns within the data, complicating analysis and interpretation.
11) Observation:
Observation is a fundamental process in the scientific method involving the systematic examination of phenomena to gather data and insights. Within machine learning, observational data is used for training models, leading to the understanding of underlying patterns and the development of more reliable predictive algorithms.
12) Knowledge:
Knowledge is the understanding and information gained through experience or education. In the context of artificial neural networks, it refers to the insights and patterns learned from training data, which the model utilizes to make future predictions. Knowledge accumulation is fundamental for improving the decision-making abilities of models.
13) Transformation (Transform, Transforming):
Transform refers to changing data from one format or representation to another, often to facilitate analysis. In neural networks, data transformation involves normalization or scaling to ensure efficient training and improved model performance. Proper transformation can enhance a model's ability to learn complex relationships within the data.
14) Measurement:
Measurement involves the systematic quantification of variables or entities in research. In the context of neural networks, accurate measurement of performance metrics, training data quality, and other parameters is crucial for assessing model effectiveness. Measurement also helps in refining algorithms and methodologies for better predictive capabilities.
15) Developing:
Developing refers to the process of creating, refining, and enhancing neural network models. It involves selecting appropriate algorithms, training on quality data, and iteratively improving performance. Effective development is critical to ensuring that models meet desired predictive goals and can adapt to varying data conditions over time.
16) Evolution:
Evolution signifies the gradual development and improvement of neural network methodologies and technologies over time. It encompasses advancements in algorithms, training processes, and applications. Understanding the evolution of neural networks helps researchers and practitioners to stay current with best practices and emerging trends.
17) Observing:
Observing involves meticulously monitoring phenomena or processes to gather pertinent data. In neural networks, observing the performance during training phases helps in identifying potential issues and making necessary adjustments. Effective observation plays a key role in ensuring accurate model predictions and optimizing learning processes.
18) Teaching:
Teaching refers to imparting knowledge or skills through structured methods. In neural networks, training can be viewed as a teaching process where the model learns from examples. Effective teaching methods enhance a model's ability to classify and predict outcomes based on historical data.
19) Relative:
Relative pertains to the idea of relationships between variables. In machine learning, understanding the relative impact of inputs on outputs is crucial for effective prediction and analysis. Neural networks learn these relationships based on data patterns, helping to establish associations essential for improving model responses.
20) Writing:
Writing in this context could refer to the documentation of algorithms, processes, or findings related to neural networks and their performance. Clear and precise writing facilitates knowledge sharing and enhances collaboration within the scientific community, ensuring the reproducibility and applicability of various methodologies.
21) Channel:
Channel broadly refers to a medium through which data or information is transmitted. In neural networks, channels can also pertain to input features, such as different data types or signals going into the model. Understanding data channels is essential for optimizing the architecture and input configurations for accurate predictions.
22) Quality:
Quality refers to the condition or standard of something, often measured against specific criteria. In neural networks, high-quality data is essential for effective training, as it directly influences the model's learning and accuracy. Ensuring data quality enhances the integrity of predictions and the overall utility of the model.
23) Harvesting (Harvest):
Harvest can symbolize the process of gathering data or insights from various sources for analysis. In the context of neural networks, 'harvesting' quality data is critical for training models effectively, as the richness and variety of the data significantly impact the derived predictions and insights.
24) Nature:
Nature refers to the inherent qualities of data and the environment from where it is derived. Understanding the nature of the data being analyzed helps in designing appropriate models and selecting suitable methodologies. It can also indicate the variability and complexity of factors impacting predictions.
25) Reason:
Reason involves the cognitive process of forming conclusions, judgments, or inferences from premises or facts. In machine learning, reasoning is essential for interpreting results, analyzing model behavior, and refining approaches. Effective reasoning enhances decision-making processes within data-driven environments and modeling contexts.
26) Desire:
Desire in this context can refer to the intention behind developing predictive models and conducting research. Understanding the desire to achieve accurate predictions drives the formulation of goals, methodologies, and potential applications, shaping the overall research strategy and influencing future directions in the field.
27) Rules:
Rules are systematic principles or guidelines that govern behavior and decision-making processes. In the context of neural networks, established rules dictate the architecture design, training procedures, and performance evaluation. Following these rules helps ensure consistency and reliability in model development and implementation.
28) Earth:
Earth might symbolize foundational concepts within the field of study, especially those relating to real-world phenomena that can be modeled using neural networks. Understanding terrestrial patterns enables NLP applications or any context analytics to influence energy consumption, weather forecasting, and similar predictive challenges.
29) Study (Studying):
Study signifies a systematic investigation into a subject to discover or revise facts and theories. In machine learning, a comprehensive study encompasses exploring algorithms, evaluating performance, and analyzing outcomes. Rigorous study enhances understanding and knowledge dissemination within the neural network research community.
30) Catching (Catch, Catched):
Catch refers to the neural network's ability to identify and learn from patterns within data. A model must effectively 'catch' relationships and dependencies to make accurate predictions. This ability enhances the model's capacity to generalize learned information to new, unseen data effectively.
31) Arma:
ARMA, or Autoregressive Moving Average, is a classical statistical method for time series analysis. While neural networks can handle non-linearities better, understanding ARMA provides context for comparing predictive methodologies. Knowledge of ARMA allows researchers to appreciate the strengths and weaknesses of different predictive techniques.
32) Tree:
Tree can symbolize decision trees, which are a type of predictive model used in statistics and machine learning. Understanding tree-based methods provides valuable insights into other modeling approaches, enhancing versatility and flexibility in selecting the most appropriate model based on specific data characteristics.
33) Hand:
Hand signifies manual intervention in the modeling process. While neural networks can automate learning and prediction, skilled human input is often necessary for configuring models, interpreting results, and fine-tuning performance. The 'hand' metaphor highlights the collaboration between automation and human expertise in the research process.
34) Post:
Post might refer to posts or articles in the academic or scientific community, disseminating findings or advancements in neural network research. Sharing insights through posts fosters collaboration, encourages knowledge exchange, and promotes continued exploration within the field of machine learning and data analysis.