### Abstract

Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain to overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN's capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, ANNs is applied in an ecological system analysis. The neural networks use the well known Back Propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The BP algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling. The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert ecosystem analyzer for many applications in ecological fields. The pilot ecosystem analyzer shows promising ability for generalization and requires further tuning and refinement of the basis neural network system for optimal performance.

Original language | English |
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Title of host publication | International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014 |

Publisher | American Institute of Physics Inc. |

Volume | 1660 |

ISBN (Electronic) | 9780735413047 |

DOIs | |

Publication status | Published - 15 May 2015 |

Event | International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014 - Penang, Malaysia Duration: 28 May 2014 → 30 May 2014 |

### Other

Other | International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014 |
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Country | Malaysia |

City | Penang |

Period | 28/5/14 → 30/5/14 |

### Fingerprint

### Keywords

- Artificial neural networks
- back propagation algorithm
- ecosystems applications

### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014*(Vol. 1660). [090017] American Institute of Physics Inc.. https://doi.org/10.1063/1.4915861

**Implementations of back propagation algorithm in ecosystems applications.** / Ali, Khalda F.; Sulaiman, Riza; Elamir, Amir Mohamed.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014.*vol. 1660, 090017, American Institute of Physics Inc., International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014, Penang, Malaysia, 28/5/14. https://doi.org/10.1063/1.4915861

}

TY - GEN

T1 - Implementations of back propagation algorithm in ecosystems applications

AU - Ali, Khalda F.

AU - Sulaiman, Riza

AU - Elamir, Amir Mohamed

PY - 2015/5/15

Y1 - 2015/5/15

N2 - Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain to overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN's capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, ANNs is applied in an ecological system analysis. The neural networks use the well known Back Propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The BP algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling. The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert ecosystem analyzer for many applications in ecological fields. The pilot ecosystem analyzer shows promising ability for generalization and requires further tuning and refinement of the basis neural network system for optimal performance.

AB - Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain to overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN's capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, ANNs is applied in an ecological system analysis. The neural networks use the well known Back Propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The BP algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling. The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert ecosystem analyzer for many applications in ecological fields. The pilot ecosystem analyzer shows promising ability for generalization and requires further tuning and refinement of the basis neural network system for optimal performance.

KW - Artificial neural networks

KW - back propagation algorithm

KW - ecosystems applications

UR - http://www.scopus.com/inward/record.url?scp=85006216370&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006216370&partnerID=8YFLogxK

U2 - 10.1063/1.4915861

DO - 10.1063/1.4915861

M3 - Conference contribution

AN - SCOPUS:85006216370

VL - 1660

BT - International Conference on Mathematics, Engineering and Industrial Applications, ICoMEIA 2014

PB - American Institute of Physics Inc.

ER -