Essay Sample on Challenges of Fitting Neural Nets

Introduction

The term neural network can also be termed as an Artificial Neural Network (ANN) with several versions of its definition. However, the central meaning of neural network remains the same. It refers to a computing system consisting of several simples. The system is made up of highly interconnected processing features whose primary purpose is to process information via dynamic state response to external inputs. These are algorithms or real hardware (processing devices) loosely modeled after the mammalian cerebral cortex neural structure but on a smaller scale. This system is the most gripping modeling approach in machine learning and has an origin of the biological neuron. This paper wills focus on the challenges of fitting neuron nets as well as providing the possible measures to mitigate these challenges.

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According to Tickle et al. (1998), fitting neurons networks, the process encompasses organized layers, and these layers are made up of numerous interconnected nodes. The nodes are further packed with activation functions; specific patterns within the network are presented via the input layer whose function is to facilitate the communication to the inner layers. The unvisitable layers link to the output layer as the result of the entire process. The process is tedious and complicated since it involves a series of data processing as well as the iterative computational runs to provide the required output. To mitigate this challenging process, experts in neural networks need to handle the process and train learner in the field of engineering on how to efficiently conduct neural network fitting. Just like universal approximators, neural networks perform best if the systems that utilize them in modeling have a high tolerance to error. Failure to adhere to this condition may lender the whole system dysfunction as a result of incompatibility. Therefore the only measure to mitigate this challenge is to ensure that the system you are using neural networks (fitting) to model must have the high tolerance to error.

Another limitation of fitting neural networks involves the backpropagation neural networks among many other types of networks. These neural networks are black boxes away from defining the architecture of neural network as well as seeding it with random figures; the system users have only one particular role of feeding it input as the system runs and awaits the feedback as the output. These types of neural networks are also slower and take much time to train compared to other types of networks (Gao et al. 2000).

Conclusion

In some instances, the backpropagation networks require several epochs. If the BPNN is simulated on a computer central processing unit (CPU), the computer CPU is responsible for computing each node's function and connect separately, and this is the origin of the problems especially in large networks containing vast volumes of data (Brown, Kass & Mitra, 2004). If they are run on a standard serial machine such as the Mac or PC, the training will cost you sometimes. However, if the BPNN is simulated on a parallel computer system, the problem can easily be mitigated since it required the user short time. However, this is not an issue with high processing speed machines.

References

Tickle, A. B., Andrews, R., Golea, M., & Diederich, J. (1998). The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9(6), 1057-1068.

Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature neuroscience, 7(5), 456.

Gao, F., Guan, X., Cao, X. R., & Papalexopoulos, A. (2000). Forecasting power market clearing price and quantity using a neural network method. In Power Engineering Society Summer Meeting, 2000. IEEE (Vol. 4, pp. 2183-2188). IEEE.