[vc_row full_width=»» parallax=»false» parallax_image=»» bg_mode=»images» gmap_lat_lng=»-7.9812985, 112.6319264″ gmap_zoom=»2″ gmap_marker_lat_lng=»-7.9812985, 112.6319264″ bg_image_repeat=»» bg_overlay=»» separator=»none»][vc_column width=»1/1″][vc_empty_space height=»50px»][/vc_column][/vc_row][vc_row full_width=»» parallax=»false» parallax_image=»» bg_mode=»images» gmap_lat_lng=»-7.9812985, 112.6319264″ gmap_zoom=»2″ gmap_marker_lat_lng=»-7.9812985, 112.6319264″ bg_image_repeat=»» bg_overlay=»» separator=»none»][vc_column width=»4/6″ css=».vc_custom_1437136542593{margin-bottom: 100px !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}» offset=»vc_hidden-xs»][vc_column_text]

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[/vc_column_text][/vc_column][vc_column width=»2/6″][vc_empty_space height=»55px»][vc_willow_button text=»Open Slideshow» link=»/report/» style=»btn-default» size=»btn-lg»][/vc_column][/vc_row][vc_row full_width=»» parallax=»false» parallax_image=»» bg_mode=»images» gmap_lat_lng=»-7.9812985, 112.6319264″ gmap_zoom=»2″ gmap_marker_lat_lng=»-7.9812985, 112.6319264″ bg_image_repeat=»» bg_overlay=»» separator=»none»][vc_column width=»1/1″][vc_willow_section_heading align=»center» large_heading=»P-network Characteristics» small_heading=»Advantages of Our Neural Net»][/vc_column][/vc_row][vc_row parallax=»0″ separator=»none» css=».vc_custom_1398397138531{padding-bottom: 90px !important;}»][vc_column width=»1/2″][vc_column_text]apple1[/vc_column_text][vc_willow_service_block style=»style-2″ heading=»Scalability»]P-network has no limitation in size or network and data complexity.

The size increase of an existing p-network has linear dependency on time and resources.[/vc_willow_service_block][vc_empty_space height=»32px»][vc_column_text][/vc_column_text][vc_willow_service_block heading=»100% Parallel Computation» style=»style-2″]The algorithms of recognition and P-network training   can be 100% parallel.

Increase in speedup is  linearly proportional to the increase of number of processors.

According to Amdahl’s law 100% parallel computation allows to use effectively any number of cores. For example, speed can be increased hundred and thousand times with various number of GPUs.[/vc_willow_service_block][vc_empty_space height=»32px»][vc_column_text][/vc_column_text][vc_willow_service_block heading=»Analog Circuit Implementation» style=»style-2″]P-network can be easily implemented on analog circuits ( new or existing electronic, optical, etc. circuits)

[/vc_willow_service_block][/vc_column][vc_column width=»1/2″][vc_column_text]aII[/vc_column_text][vc_willow_service_block heading=»Extremely High Training Speed» style=»style-2″]The training time increases linearly when the number of data sets increases, compared to an exponential increase of other neural networks.[/vc_willow_service_block][vc_empty_space height=»32px»][vc_column_text][/vc_column_text][vc_willow_service_block heading=»Continuous Learning» style=»style-2″]Trained P-network can be continuously trained on new data sets.[/vc_willow_service_block][vc_empty_space height=»32px»][vc_column_text][/vc_column_text][vc_willow_service_block heading=»Structure Reflective of a Biological Neuron Structure» style=»style-2″]Patented Internal Feedback training method, as an alternative to Back Propagation method.

It allows for multiple error definition within one epoch.

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