It is important to consider temporal changes as well as spatial differences in ecological studies. Temporal changes can be effectively elucidated by modeling using temporal neural networks. Different characteristics are observed in temporal patterns and static patterns. A static pattern is viewed as a random point in an ^-dimensional space, whereas a temporal sequence, as a function of time, must be order sensitive. Therefore, memoryless networks used in static networks are inadequate for temporal pattern recognition, because time must play a prominent role. The temporal model should be chosen to adequately show temporal characteristics. Temporal artifical neural networks (ANNs) can be placed into two categories, supervised and unsupervised temporal networks, based on their learning algorithms.

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